Based on the ability of user-defined functions, IoTDB provides a series of functions for temporal data processing, including data quality, data profiling, anomaly detection, frequency domain analysis, data matching, data repairing, sequence discovery, machine learning, etc., which can meet the needs of industrial fields for temporal data processing.
Note: The functions in the current UDF library only support millisecond level timestamp accuracy.
Please obtain the compressed file of the UDF library JAR package that is compatible with the IoTDB version.
| UDF installation package | Supported IoTDB versions | Download link |
|---|---|---|
| apache-UDF-1.3.3.zip | V1.3.3 and above | Please contact Timecho for assistance |
| apache-UDF-1.3.2.zip | V1.0.0～V1.3.2 | Please contact Timecho for assistance |
Place the library-udf.jar file in the compressed file obtained in the directory /ext/udf of all nodes in the IoTDB cluster
In the SQL operation interface of IoTDB's SQL command line terminal (CLI), execute the corresponding function registration statement as follows.
Batch registration: Two registration methods: registration script or SQL full statement
Register Script
tools directory of IoTDB as needed, and modify the parameters in the script (default is host=127.0.0.1, rpcPort=6667, user=root, pass=root);All SQL statements
create function completeness as 'org.apache.iotdb.library.dquality.UDTFCompleteness'
This function is used to calculate the completeness of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the completeness of each window will be output.
Name: COMPLETENESS
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
window: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. By default, all input data belongs to the same window.downtime: Whether the downtime exception is considered in the calculation of completeness. It is ‘true’ or ‘false’ (default). When considering the downtime exception, long-term missing data will be considered as downtime exception without any influence on completeness.Output Series: Output a single series. The type is DOUBLE. The range of each value is [0,1].
Note: Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output.
With default parameters, this function will regard all input data as the same window.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| +-----------------------------+---------------+
SQL for query:
select completeness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
Output series:
+-----------------------------+-----------------------------+ | Time|completeness(root.test.d1.s1)| +-----------------------------+-----------------------------+ |2020-01-01T00:00:02.000+08:00| 0.875| +-----------------------------+-----------------------------+
When the window size is given, this function will divide the input data as multiple windows.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| |2020-01-01T00:00:32.000+08:00| 130.0| |2020-01-01T00:00:34.000+08:00| 132.0| |2020-01-01T00:00:36.000+08:00| 134.0| |2020-01-01T00:00:38.000+08:00| 136.0| |2020-01-01T00:00:40.000+08:00| 138.0| |2020-01-01T00:00:42.000+08:00| 140.0| |2020-01-01T00:00:44.000+08:00| 142.0| |2020-01-01T00:00:46.000+08:00| 144.0| |2020-01-01T00:00:48.000+08:00| 146.0| |2020-01-01T00:00:50.000+08:00| 148.0| |2020-01-01T00:00:52.000+08:00| 150.0| |2020-01-01T00:00:54.000+08:00| 152.0| |2020-01-01T00:00:56.000+08:00| 154.0| |2020-01-01T00:00:58.000+08:00| 156.0| |2020-01-01T00:01:00.000+08:00| 158.0| +-----------------------------+---------------+
SQL for query:
select completeness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00
Output series:
+-----------------------------+--------------------------------------------+ | Time|completeness(root.test.d1.s1, "window"="15")| +-----------------------------+--------------------------------------------+ |2020-01-01T00:00:02.000+08:00| 0.875| |2020-01-01T00:00:32.000+08:00| 1.0| +-----------------------------+--------------------------------------------+
create function consistency as 'org.apache.iotdb.library.dquality.UDTFConsistency'
This function is used to calculate the consistency of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the consistency of each window will be output.
Name: CONSISTENCY
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
window: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. By default, all input data belongs to the same window.Output Series: Output a single series. The type is DOUBLE. The range of each value is [0,1].
Note: Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output.
With default parameters, this function will regard all input data as the same window.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| +-----------------------------+---------------+
SQL for query:
select consistency(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
Output series:
+-----------------------------+----------------------------+ | Time|consistency(root.test.d1.s1)| +-----------------------------+----------------------------+ |2020-01-01T00:00:02.000+08:00| 0.9333333333333333| +-----------------------------+----------------------------+
When the window size is given, this function will divide the input data as multiple windows.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| |2020-01-01T00:00:32.000+08:00| 130.0| |2020-01-01T00:00:34.000+08:00| 132.0| |2020-01-01T00:00:36.000+08:00| 134.0| |2020-01-01T00:00:38.000+08:00| 136.0| |2020-01-01T00:00:40.000+08:00| 138.0| |2020-01-01T00:00:42.000+08:00| 140.0| |2020-01-01T00:00:44.000+08:00| 142.0| |2020-01-01T00:00:46.000+08:00| 144.0| |2020-01-01T00:00:48.000+08:00| 146.0| |2020-01-01T00:00:50.000+08:00| 148.0| |2020-01-01T00:00:52.000+08:00| 150.0| |2020-01-01T00:00:54.000+08:00| 152.0| |2020-01-01T00:00:56.000+08:00| 154.0| |2020-01-01T00:00:58.000+08:00| 156.0| |2020-01-01T00:01:00.000+08:00| 158.0| +-----------------------------+---------------+
SQL for query:
select consistency(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00
Output series:
+-----------------------------+-------------------------------------------+ | Time|consistency(root.test.d1.s1, "window"="15")| +-----------------------------+-------------------------------------------+ |2020-01-01T00:00:02.000+08:00| 0.9333333333333333| |2020-01-01T00:00:32.000+08:00| 1.0| +-----------------------------+-------------------------------------------+
create function timeliness as 'org.apache.iotdb.library.dquality.UDTFTimeliness'
This function is used to calculate the timeliness of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the timeliness of each window will be output.
Name: TIMELINESS
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
window: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. By default, all input data belongs to the same window.Output Series: Output a single series. The type is DOUBLE. The range of each value is [0,1].
Note: Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output.
With default parameters, this function will regard all input data as the same window.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| +-----------------------------+---------------+
SQL for query:
select timeliness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
Output series:
+-----------------------------+---------------------------+ | Time|timeliness(root.test.d1.s1)| +-----------------------------+---------------------------+ |2020-01-01T00:00:02.000+08:00| 0.9333333333333333| +-----------------------------+---------------------------+
When the window size is given, this function will divide the input data as multiple windows.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| |2020-01-01T00:00:32.000+08:00| 130.0| |2020-01-01T00:00:34.000+08:00| 132.0| |2020-01-01T00:00:36.000+08:00| 134.0| |2020-01-01T00:00:38.000+08:00| 136.0| |2020-01-01T00:00:40.000+08:00| 138.0| |2020-01-01T00:00:42.000+08:00| 140.0| |2020-01-01T00:00:44.000+08:00| 142.0| |2020-01-01T00:00:46.000+08:00| 144.0| |2020-01-01T00:00:48.000+08:00| 146.0| |2020-01-01T00:00:50.000+08:00| 148.0| |2020-01-01T00:00:52.000+08:00| 150.0| |2020-01-01T00:00:54.000+08:00| 152.0| |2020-01-01T00:00:56.000+08:00| 154.0| |2020-01-01T00:00:58.000+08:00| 156.0| |2020-01-01T00:01:00.000+08:00| 158.0| +-----------------------------+---------------+
SQL for query:
select timeliness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00
Output series:
+-----------------------------+------------------------------------------+ | Time|timeliness(root.test.d1.s1, "window"="15")| +-----------------------------+------------------------------------------+ |2020-01-01T00:00:02.000+08:00| 0.9333333333333333| |2020-01-01T00:00:32.000+08:00| 1.0| +-----------------------------+------------------------------------------+
create function validity as 'org.apache.iotdb.library.dquality.UDTFValidity'
This function is used to calculate the Validity of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the Validity of each window will be output.
Name: VALIDITY
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
window: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. By default, all input data belongs to the same window.Output Series: Output a single series. The type is DOUBLE. The range of each value is [0,1].
Note: Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output.
With default parameters, this function will regard all input data as the same window.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| +-----------------------------+---------------+
SQL for query:
select Validity(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
Output series:
+-----------------------------+-------------------------+ | Time|validity(root.test.d1.s1)| +-----------------------------+-------------------------+ |2020-01-01T00:00:02.000+08:00| 0.8833333333333333| +-----------------------------+-------------------------+
When the window size is given, this function will divide the input data as multiple windows.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| |2020-01-01T00:00:32.000+08:00| 130.0| |2020-01-01T00:00:34.000+08:00| 132.0| |2020-01-01T00:00:36.000+08:00| 134.0| |2020-01-01T00:00:38.000+08:00| 136.0| |2020-01-01T00:00:40.000+08:00| 138.0| |2020-01-01T00:00:42.000+08:00| 140.0| |2020-01-01T00:00:44.000+08:00| 142.0| |2020-01-01T00:00:46.000+08:00| 144.0| |2020-01-01T00:00:48.000+08:00| 146.0| |2020-01-01T00:00:50.000+08:00| 148.0| |2020-01-01T00:00:52.000+08:00| 150.0| |2020-01-01T00:00:54.000+08:00| 152.0| |2020-01-01T00:00:56.000+08:00| 154.0| |2020-01-01T00:00:58.000+08:00| 156.0| |2020-01-01T00:01:00.000+08:00| 158.0| +-----------------------------+---------------+
SQL for query:
select Validity(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00
Output series:
+-----------------------------+----------------------------------------+ | Time|validity(root.test.d1.s1, "window"="15")| +-----------------------------+----------------------------------------+ |2020-01-01T00:00:02.000+08:00| 0.8833333333333333| |2020-01-01T00:00:32.000+08:00| 1.0| +-----------------------------+----------------------------------------+
create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF'
This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. For more information, please refer to XCorr function.
Name: ACF
Input Series: Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series. The type is DOUBLE. There are $2N-1$ data points in the series, and the values are interpreted in details in XCorr function.
Note:
null and NaN values in the input series will be ignored and treated as 0.Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:01.000+08:00| 1| |2020-01-01T00:00:02.000+08:00| null| |2020-01-01T00:00:03.000+08:00| 3| |2020-01-01T00:00:04.000+08:00| NaN| |2020-01-01T00:00:05.000+08:00| 5| +-----------------------------+---------------+
SQL for query:
select acf(s1) from root.test.d1 where time <= 2020-01-01 00:00:05
Output series:
+-----------------------------+--------------------+ | Time|acf(root.test.d1.s1)| +-----------------------------+--------------------+ |1970-01-01T08:00:00.001+08:00| 1.0| |1970-01-01T08:00:00.002+08:00| 0.0| |1970-01-01T08:00:00.003+08:00| 3.6| |1970-01-01T08:00:00.004+08:00| 0.0| |1970-01-01T08:00:00.005+08:00| 7.0| |1970-01-01T08:00:00.006+08:00| 0.0| |1970-01-01T08:00:00.007+08:00| 3.6| |1970-01-01T08:00:00.008+08:00| 0.0| |1970-01-01T08:00:00.009+08:00| 1.0| +-----------------------------+--------------------+
create function distinct as 'org.apache.iotdb.library.dprofile.UDTFDistinct'
This function returns all unique values in time series.
Name: DISTINCT
Input Series: Only support a single input series. The type is arbitrary.
Output Series: Output a single series. The type is the same as the input.
Note:
NaN will not.Input series:
+-----------------------------+---------------+ | Time|root.test.d2.s2| +-----------------------------+---------------+ |2020-01-01T08:00:00.001+08:00| Hello| |2020-01-01T08:00:00.002+08:00| hello| |2020-01-01T08:00:00.003+08:00| Hello| |2020-01-01T08:00:00.004+08:00| World| |2020-01-01T08:00:00.005+08:00| World| +-----------------------------+---------------+
SQL for query:
select distinct(s2) from root.test.d2
Output series:
+-----------------------------+-------------------------+ | Time|distinct(root.test.d2.s2)| +-----------------------------+-------------------------+ |1970-01-01T08:00:00.001+08:00| Hello| |1970-01-01T08:00:00.002+08:00| hello| |1970-01-01T08:00:00.003+08:00| World| +-----------------------------+-------------------------+
create function histogram as 'org.apache.iotdb.library.dprofile.UDTFHistogram'
This function is used to calculate the distribution histogram of a single column of numerical data.
Name: HISTOGRAM
Input Series: Only supports a single input sequence, the type is INT32 / INT64 / FLOAT / DOUBLE
Parameters:
min: The lower limit of the requested data range, the default value is -Double.MAX_VALUE.max: The upper limit of the requested data range, the default value is Double.MAX_VALUE, and the value of start must be less than or equal to end.count: The number of buckets of the histogram, the default value is 1. It must be a positive integer.Output Series: The value of the bucket of the histogram, where the lower bound represented by the i-th bucket (index starts from 1) is $min+ (i-1)\cdot\frac{max-min}{count}$ and the upper bound is $min + i \cdot \frac{max-min}{count}$.
Note:
min, it will be put into the 1st bucket. If the value is larger than max, it will be put into the last bucket.NaN in the input series will be ignored.Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:00.000+08:00| 1.0| |2020-01-01T00:00:01.000+08:00| 2.0| |2020-01-01T00:00:02.000+08:00| 3.0| |2020-01-01T00:00:03.000+08:00| 4.0| |2020-01-01T00:00:04.000+08:00| 5.0| |2020-01-01T00:00:05.000+08:00| 6.0| |2020-01-01T00:00:06.000+08:00| 7.0| |2020-01-01T00:00:07.000+08:00| 8.0| |2020-01-01T00:00:08.000+08:00| 9.0| |2020-01-01T00:00:09.000+08:00| 10.0| |2020-01-01T00:00:10.000+08:00| 11.0| |2020-01-01T00:00:11.000+08:00| 12.0| |2020-01-01T00:00:12.000+08:00| 13.0| |2020-01-01T00:00:13.000+08:00| 14.0| |2020-01-01T00:00:14.000+08:00| 15.0| |2020-01-01T00:00:15.000+08:00| 16.0| |2020-01-01T00:00:16.000+08:00| 17.0| |2020-01-01T00:00:17.000+08:00| 18.0| |2020-01-01T00:00:18.000+08:00| 19.0| |2020-01-01T00:00:19.000+08:00| 20.0| +-----------------------------+---------------+
SQL for query:
select histogram(s1,"min"="1","max"="20","count"="10") from root.test.d1
Output series:
+-----------------------------+---------------------------------------------------------------+ | Time|histogram(root.test.d1.s1, "min"="1", "max"="20", "count"="10")| +-----------------------------+---------------------------------------------------------------+ |1970-01-01T08:00:00.000+08:00| 2| |1970-01-01T08:00:00.001+08:00| 2| |1970-01-01T08:00:00.002+08:00| 2| |1970-01-01T08:00:00.003+08:00| 2| |1970-01-01T08:00:00.004+08:00| 2| |1970-01-01T08:00:00.005+08:00| 2| |1970-01-01T08:00:00.006+08:00| 2| |1970-01-01T08:00:00.007+08:00| 2| |1970-01-01T08:00:00.008+08:00| 2| |1970-01-01T08:00:00.009+08:00| 2| +-----------------------------+---------------------------------------------------------------+
create function integral as 'org.apache.iotdb.library.dprofile.UDAFIntegral'
This function is used to calculate the integration of time series, which equals to the area under the curve with time as X-axis and values as Y-axis.
Name: INTEGRAL
Input Series: Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
unit: The unit of time used when computing the integral. The value should be chosen from “1S”, “1s”, “1m”, “1H”, “1d”(case-sensitive), and each represents taking one millisecond / second / minute / hour / day as 1.0 while calculating the area and integral.Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the integration.
Note:
The integral value equals to the sum of the areas of right-angled trapezoids consisting of each two adjacent points and the time-axis. Choosing different unit implies different scaling of time axis, thus making it apparent to convert the value among those results with constant coefficient.
NaN values in the input series will be ignored. The curve or trapezoids will skip these points and use the next valid point.
With default parameters, this function will take one second as 1.0.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:01.000+08:00| 1| |2020-01-01T00:00:02.000+08:00| 2| |2020-01-01T00:00:03.000+08:00| 5| |2020-01-01T00:00:04.000+08:00| 6| |2020-01-01T00:00:05.000+08:00| 7| |2020-01-01T00:00:08.000+08:00| 8| |2020-01-01T00:00:09.000+08:00| NaN| |2020-01-01T00:00:10.000+08:00| 10| +-----------------------------+---------------+
SQL for query:
select integral(s1) from root.test.d1 where time <= 2020-01-01 00:00:10
Output series:
+-----------------------------+-------------------------+ | Time|integral(root.test.d1.s1)| +-----------------------------+-------------------------+ |1970-01-01T08:00:00.000+08:00| 57.5| +-----------------------------+-------------------------+
Calculation expression: $$\frac{1}{2}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] = 57.5$$
With time unit specified as “1m”, this function will take one minute as 1.0.
Input series is the same as above, the SQL for query is shown below:
select integral(s1, "unit"="1m") from root.test.d1 where time <= 2020-01-01 00:00:10
Output series:
+-----------------------------+-------------------------+ | Time|integral(root.test.d1.s1)| +-----------------------------+-------------------------+ |1970-01-01T08:00:00.000+08:00| 0.958| +-----------------------------+-------------------------+
Calculation expression: $$\frac{1}{2\times 60}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] = 0.958$$
create function integralavg as 'org.apache.iotdb.library.dprofile.UDAFIntegralAvg'
This function is used to calculate the function average of time series. The output equals to the area divided by the time interval using the same time unit. For more information of the area under the curve, please refer to Integral function.
Name: INTEGRALAVG
Input Series: Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the time-weighted average.
Note:
The time-weighted value equals to the integral value with any unit divided by the time interval of input series. The result is irrelevant to the time unit used in integral, and it's consistent with the timestamp precision of IoTDB by default.
NaN values in the input series will be ignored. The curve or trapezoids will skip these points and use the next valid point.
If the input series is empty, the output value will be 0.0, but if there is only one data point, the value will equal to the input value.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:01.000+08:00| 1| |2020-01-01T00:00:02.000+08:00| 2| |2020-01-01T00:00:03.000+08:00| 5| |2020-01-01T00:00:04.000+08:00| 6| |2020-01-01T00:00:05.000+08:00| 7| |2020-01-01T00:00:08.000+08:00| 8| |2020-01-01T00:00:09.000+08:00| NaN| |2020-01-01T00:00:10.000+08:00| 10| +-----------------------------+---------------+
SQL for query:
select integralavg(s1) from root.test.d1 where time <= 2020-01-01 00:00:10
Output series:
+-----------------------------+----------------------------+ | Time|integralavg(root.test.d1.s1)| +-----------------------------+----------------------------+ |1970-01-01T08:00:00.000+08:00| 5.75| +-----------------------------+----------------------------+
Calculation expression: $$\frac{1}{2}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] / 10 = 5.75$$
create function mad as 'org.apache.iotdb.library.dprofile.UDAFMad'
The function is used to compute the exact or approximate median absolute deviation (MAD) of a numeric time series. MAD is the median of the deviation of each element from the elements' median.
Take a dataset ${1,3,3,5,5,6,7,8,9}$ as an instance. Its median is 5 and the deviation of each element from the median is ${0,0,1,2,2,2,3,4,4}$, whose median is 2. Therefore, the MAD of the original dataset is 2.
Name: MAD
Input Series: Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
Parameter:
error: The relative error of the approximate MAD. It should be within [0,1) and the default value is 0. Taking error=0.01 as an instance, suppose the exact MAD is $a$ and the approximate MAD is $b$, we have $0.99a \le b \le 1.01a$. With error=0, the output is the exact MAD.Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the MAD.
Note: Missing points, null points and NaN in the input series will be ignored.
With the default error(error=0), the function queries the exact MAD.
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |1970-01-01T08:00:00.100+08:00| 0.0| |1970-01-01T08:00:00.200+08:00| 0.0| |1970-01-01T08:00:00.300+08:00| 1.0| |1970-01-01T08:00:00.400+08:00| -1.0| |1970-01-01T08:00:00.500+08:00| 0.0| |1970-01-01T08:00:00.600+08:00| 0.0| |1970-01-01T08:00:00.700+08:00| -2.0| |1970-01-01T08:00:00.800+08:00| 2.0| |1970-01-01T08:00:00.900+08:00| 0.0| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 1.0| |1970-01-01T08:00:01.200+08:00| -1.0| |1970-01-01T08:00:01.300+08:00| -1.0| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 0.0| |1970-01-01T08:00:01.600+08:00| 0.0| |1970-01-01T08:00:01.700+08:00| 10.0| |1970-01-01T08:00:01.800+08:00| 2.0| |1970-01-01T08:00:01.900+08:00| -2.0| |1970-01-01T08:00:02.000+08:00| 0.0| +-----------------------------+------------+ ............ Total line number = 20
SQL for query:
select mad(s1) from root.test
Output series:
+-----------------------------+---------------------------------+ | Time|median(root.test.s1, "error"="0")| +-----------------------------+---------------------------------+ |1970-01-01T08:00:00.000+08:00| 0.0| +-----------------------------+---------------------------------+
By setting error within (0,1), the function queries the approximate MAD.
SQL for query:
select mad(s1, "error"="0.01") from root.test
Output series:
+-----------------------------+---------------------------------+ | Time|mad(root.test.s1, "error"="0.01")| +-----------------------------+---------------------------------+ |1970-01-01T08:00:00.000+08:00| 0.9900000000000001| +-----------------------------+---------------------------------+
create function median as 'org.apache.iotdb.library.dprofile.UDAFMedian'
The function is used to compute the exact or approximate median of a numeric time series. Median is the value separating the higher half from the lower half of a data sample.
Name: MEDIAN
Input Series: Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
Parameter:
error: The rank error of the approximate median. It should be within [0,1) and the default value is 0. For instance, a median with error=0.01 is the value of the element with rank percentage 0.49~0.51. With error=0, the output is the exact median.Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the median.
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |1970-01-01T08:00:00.100+08:00| 0.0| |1970-01-01T08:00:00.200+08:00| 0.0| |1970-01-01T08:00:00.300+08:00| 1.0| |1970-01-01T08:00:00.400+08:00| -1.0| |1970-01-01T08:00:00.500+08:00| 0.0| |1970-01-01T08:00:00.600+08:00| 0.0| |1970-01-01T08:00:00.700+08:00| -2.0| |1970-01-01T08:00:00.800+08:00| 2.0| |1970-01-01T08:00:00.900+08:00| 0.0| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 1.0| |1970-01-01T08:00:01.200+08:00| -1.0| |1970-01-01T08:00:01.300+08:00| -1.0| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 0.0| |1970-01-01T08:00:01.600+08:00| 0.0| |1970-01-01T08:00:01.700+08:00| 10.0| |1970-01-01T08:00:01.800+08:00| 2.0| |1970-01-01T08:00:01.900+08:00| -2.0| |1970-01-01T08:00:02.000+08:00| 0.0| +-----------------------------+------------+ Total line number = 20
SQL for query:
select median(s1, "error"="0.01") from root.test
Output series:
+-----------------------------+------------------------------------+ | Time|median(root.test.s1, "error"="0.01")| +-----------------------------+------------------------------------+ |1970-01-01T08:00:00.000+08:00| 0.0| +-----------------------------+------------------------------------+
create function minmax as 'org.apache.iotdb.library.dprofile.UDTFMinMax'
This function is used to standardize the input series with min-max. Minimum value is transformed to 0; maximum value is transformed to 1.
Name: MINMAX
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
compute: When set to “batch”, anomaly test is conducted after importing all data points; when set to “stream”, it is required to provide minimum and maximum values. The default method is “batch”.min: The maximum value when method is set to “stream”.max: The minimum value when method is set to “stream”.Output Series: Output a single series. The type is DOUBLE.
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |1970-01-01T08:00:00.100+08:00| 0.0| |1970-01-01T08:00:00.200+08:00| 0.0| |1970-01-01T08:00:00.300+08:00| 1.0| |1970-01-01T08:00:00.400+08:00| -1.0| |1970-01-01T08:00:00.500+08:00| 0.0| |1970-01-01T08:00:00.600+08:00| 0.0| |1970-01-01T08:00:00.700+08:00| -2.0| |1970-01-01T08:00:00.800+08:00| 2.0| |1970-01-01T08:00:00.900+08:00| 0.0| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 1.0| |1970-01-01T08:00:01.200+08:00| -1.0| |1970-01-01T08:00:01.300+08:00| -1.0| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 0.0| |1970-01-01T08:00:01.600+08:00| 0.0| |1970-01-01T08:00:01.700+08:00| 10.0| |1970-01-01T08:00:01.800+08:00| 2.0| |1970-01-01T08:00:01.900+08:00| -2.0| |1970-01-01T08:00:02.000+08:00| 0.0| +-----------------------------+------------+
SQL for query:
select minmax(s1) from root.test
Output series:
+-----------------------------+--------------------+ | Time|minmax(root.test.s1)| +-----------------------------+--------------------+ |1970-01-01T08:00:00.100+08:00| 0.16666666666666666| |1970-01-01T08:00:00.200+08:00| 0.16666666666666666| |1970-01-01T08:00:00.300+08:00| 0.25| |1970-01-01T08:00:00.400+08:00| 0.08333333333333333| |1970-01-01T08:00:00.500+08:00| 0.16666666666666666| |1970-01-01T08:00:00.600+08:00| 0.16666666666666666| |1970-01-01T08:00:00.700+08:00| 0.0| |1970-01-01T08:00:00.800+08:00| 0.3333333333333333| |1970-01-01T08:00:00.900+08:00| 0.16666666666666666| |1970-01-01T08:00:01.000+08:00| 0.16666666666666666| |1970-01-01T08:00:01.100+08:00| 0.25| |1970-01-01T08:00:01.200+08:00| 0.08333333333333333| |1970-01-01T08:00:01.300+08:00| 0.08333333333333333| |1970-01-01T08:00:01.400+08:00| 0.25| |1970-01-01T08:00:01.500+08:00| 0.16666666666666666| |1970-01-01T08:00:01.600+08:00| 0.16666666666666666| |1970-01-01T08:00:01.700+08:00| 1.0| |1970-01-01T08:00:01.800+08:00| 0.3333333333333333| |1970-01-01T08:00:01.900+08:00| 0.0| |1970-01-01T08:00:02.000+08:00| 0.16666666666666666| +-----------------------------+--------------------+
create function mvavg as 'org.apache.iotdb.library.dprofile.UDTFMvAvg'
This function is used to calculate moving average of input series.
Name: MVAVG
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
window: Length of the moving window. Default value is 10.Output Series: Output a single series. The type is DOUBLE.
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |1970-01-01T08:00:00.100+08:00| 0.0| |1970-01-01T08:00:00.200+08:00| 0.0| |1970-01-01T08:00:00.300+08:00| 1.0| |1970-01-01T08:00:00.400+08:00| -1.0| |1970-01-01T08:00:00.500+08:00| 0.0| |1970-01-01T08:00:00.600+08:00| 0.0| |1970-01-01T08:00:00.700+08:00| -2.0| |1970-01-01T08:00:00.800+08:00| 2.0| |1970-01-01T08:00:00.900+08:00| 0.0| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 1.0| |1970-01-01T08:00:01.200+08:00| -1.0| |1970-01-01T08:00:01.300+08:00| -1.0| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 0.0| |1970-01-01T08:00:01.600+08:00| 0.0| |1970-01-01T08:00:01.700+08:00| 10.0| |1970-01-01T08:00:01.800+08:00| 2.0| |1970-01-01T08:00:01.900+08:00| -2.0| |1970-01-01T08:00:02.000+08:00| 0.0| +-----------------------------+------------+
SQL for query:
select mvavg(s1, "window"="3") from root.test
Output series:
+-----------------------------+---------------------------------+ | Time|mvavg(root.test.s1, "window"="3")| +-----------------------------+---------------------------------+ |1970-01-01T08:00:00.300+08:00| 0.3333333333333333| |1970-01-01T08:00:00.400+08:00| 0.0| |1970-01-01T08:00:00.500+08:00| -0.3333333333333333| |1970-01-01T08:00:00.600+08:00| 0.0| |1970-01-01T08:00:00.700+08:00| -0.6666666666666666| |1970-01-01T08:00:00.800+08:00| 0.0| |1970-01-01T08:00:00.900+08:00| 0.6666666666666666| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 0.3333333333333333| |1970-01-01T08:00:01.200+08:00| 0.0| |1970-01-01T08:00:01.300+08:00| -0.6666666666666666| |1970-01-01T08:00:01.400+08:00| 0.0| |1970-01-01T08:00:01.500+08:00| 0.3333333333333333| |1970-01-01T08:00:01.600+08:00| 0.0| |1970-01-01T08:00:01.700+08:00| 3.3333333333333335| |1970-01-01T08:00:01.800+08:00| 4.0| |1970-01-01T08:00:01.900+08:00| 0.0| |1970-01-01T08:00:02.000+08:00| -0.6666666666666666| +-----------------------------+---------------------------------+
create function pacf as 'org.apache.iotdb.library.dprofile.UDTFPACF'
This function is used to calculate partial autocorrelation of input series by solving Yule-Walker equation. For some cases, the equation may not be solved, and NaN will be output.
Name: PACF
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
lag: Maximum lag of pacf to calculate. The default value is $\min(10\log_{10}n,n-1)$, where $n$ is the number of data points.Output Series: Output a single series. The type is DOUBLE.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:01.000+08:00| 1| |2020-01-01T00:00:02.000+08:00| NaN| |2020-01-01T00:00:03.000+08:00| 3| |2020-01-01T00:00:04.000+08:00| NaN| |2020-01-01T00:00:05.000+08:00| 5| +-----------------------------+---------------+
SQL for query:
select pacf(s1, "lag"="5") from root.test.d1
Output series:
+-----------------------------+--------------------------------+ | Time|pacf(root.test.d1.s1, "lag"="5")| +-----------------------------+--------------------------------+ |2020-01-01T00:00:01.000+08:00| 1.0| |2020-01-01T00:00:02.000+08:00| -0.5744680851063829| |2020-01-01T00:00:03.000+08:00| 0.3172297297297296| |2020-01-01T00:00:04.000+08:00| -0.2977686586304181| |2020-01-01T00:00:05.000+08:00| -2.0609033521065867| +-----------------------------+--------------------------------+
create function percentile as 'org.apache.iotdb.library.dprofile.UDAFPercentile'
The function is used to compute the exact or approximate percentile of a numeric time series. A percentile is value of element in the certain rank of the sorted series.
Name: PERCENTILE
Input Series: Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
Parameter:
rank: The rank percentage of the percentile. It should be (0,1] and the default value is 0.5. For instance, a percentile with rank=0.5 is the median.error: The rank error of the approximate percentile. It should be within [0,1) and the default value is 0. For instance, a 0.5-percentile with error=0.01 is the value of the element with rank percentage 0.49~0.51. With error=0, the output is the exact percentile.Output Series: Output a single series. The type is the same as input series. If error=0, there is only one data point in the series, whose timestamp is the same has which the first percentile value has, and value is the percentile, otherwise the timestamp of the only data point is 0.
Note: Missing points, null points and NaN in the input series will be ignored.
Input series:
+-----------------------------+-------------+ | Time|root.test2.s1| +-----------------------------+-------------+ |1970-01-01T08:00:00.100+08:00| 0.0| |1970-01-01T08:00:00.200+08:00| 0.0| |1970-01-01T08:00:00.300+08:00| 1.0| |1970-01-01T08:00:00.400+08:00| -1.0| |1970-01-01T08:00:00.500+08:00| 0.0| |1970-01-01T08:00:00.600+08:00| 0.0| |1970-01-01T08:00:00.700+08:00| -2.0| |1970-01-01T08:00:00.800+08:00| 2.0| |1970-01-01T08:00:00.900+08:00| 0.0| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 1.0| |1970-01-01T08:00:01.200+08:00| -1.0| |1970-01-01T08:00:01.300+08:00| -1.0| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 0.0| |1970-01-01T08:00:01.600+08:00| 0.0| |1970-01-01T08:00:01.700+08:00| 10.0| |1970-01-01T08:00:01.800+08:00| 2.0| |1970-01-01T08:00:01.900+08:00| -2.0| |1970-01-01T08:00:02.000+08:00| 0.0| +-----------------------------+-------------+ Total line number = 20
SQL for query:
select percentile(s0, "rank"="0.2", "error"="0.01") from root.test
Output series:
+-----------------------------+-------------------------------------------------------+ | Time|percentile(root.test2.s1, "rank"="0.2", "error"="0.01")| +-----------------------------+-------------------------------------------------------+ |1970-01-01T08:00:00.000+08:00| -1.0| +-----------------------------+-------------------------------------------------------+
create function quantile as 'org.apache.iotdb.library.dprofile.UDAFQuantile'
The function is used to compute the approximate quantile of a numeric time series. A quantile is value of element in the certain rank of the sorted series.
Name: QUANTILE
Input Series: Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
Parameter:
rank: The rank of the quantile. It should be (0,1] and the default value is 0.5. For instance, a quantile with rank=0.5 is the median.K: The size of KLL sketch maintained in the query. It should be within [100,+inf) and the default value is 800. For instance, the 0.5-quantile computed by a KLL sketch with K=800 items is a value with rank quantile 0.49~0.51 with a confidence of at least 99%. The result will be more accurate as K increases.Output Series: Output a single series. The type is the same as input series. The timestamp of the only data point is 0.
Note: Missing points, null points and NaN in the input series will be ignored.
Input series:
+-----------------------------+-------------+ | Time|root.test1.s1| +-----------------------------+-------------+ |2021-03-17T10:32:17.054+08:00| 7| |2021-03-17T10:32:18.054+08:00| 15| |2021-03-17T10:32:19.054+08:00| 36| |2021-03-17T10:32:20.054+08:00| 39| |2021-03-17T10:32:21.054+08:00| 40| |2021-03-17T10:32:22.054+08:00| 41| |2021-03-17T10:32:23.054+08:00| 20| |2021-03-17T10:32:24.054+08:00| 18| +-----------------------------+-------------+ ............ Total line number = 8
SQL for query:
select quantile(s1, "rank"="0.2", "K"="800") from root.test1
Output series:
+-----------------------------+------------------------------------------------+ | Time|quantile(root.test1.s1, "rank"="0.2", "K"="800")| +-----------------------------+------------------------------------------------+ |1970-01-01T08:00:00.000+08:00| 7.000000000000001| +-----------------------------+------------------------------------------------+
create function period as 'org.apache.iotdb.library.dprofile.UDAFPeriod'
The function is used to compute the period of a numeric time series.
Name: PERIOD
Input Series: Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series. The type is INT32. There is only one data point in the series, whose timestamp is 0 and value is the period.
Input series:
+-----------------------------+---------------+ | Time|root.test.d3.s1| +-----------------------------+---------------+ |1970-01-01T08:00:00.001+08:00| 1.0| |1970-01-01T08:00:00.002+08:00| 2.0| |1970-01-01T08:00:00.003+08:00| 3.0| |1970-01-01T08:00:00.004+08:00| 1.0| |1970-01-01T08:00:00.005+08:00| 2.0| |1970-01-01T08:00:00.006+08:00| 3.0| |1970-01-01T08:00:00.007+08:00| 1.0| |1970-01-01T08:00:00.008+08:00| 2.0| |1970-01-01T08:00:00.009+08:00| 3.0| +-----------------------------+---------------+
SQL for query:
select period(s1) from root.test.d3
Output series:
+-----------------------------+-----------------------+ | Time|period(root.test.d3.s1)| +-----------------------------+-----------------------+ |1970-01-01T08:00:00.000+08:00| 3| +-----------------------------+-----------------------+
create function qlb as 'org.apache.iotdb.library.dprofile.UDTFQLB'
This function is used to calculate Ljung-Box statistics $Q_{LB}$ for time series, and convert it to p value.
Name: QLB
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
lag: max lag to calculate. Legal input shall be integer from 1 to n-2, where n is the sample number. Default value is n-2.
Output Series: Output a single series. The type is DOUBLE. The output series is p value, and timestamp means lag.
Note: If you want to calculate Ljung-Box statistics $Q_{LB}$ instead of p value, you may use ACF function.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |1970-01-01T00:00:00.100+08:00| 1.22| |1970-01-01T00:00:00.200+08:00| -2.78| |1970-01-01T00:00:00.300+08:00| 1.53| |1970-01-01T00:00:00.400+08:00| 0.70| |1970-01-01T00:00:00.500+08:00| 0.75| |1970-01-01T00:00:00.600+08:00| -0.72| |1970-01-01T00:00:00.700+08:00| -0.22| |1970-01-01T00:00:00.800+08:00| 0.28| |1970-01-01T00:00:00.900+08:00| 0.57| |1970-01-01T00:00:01.000+08:00| -0.22| |1970-01-01T00:00:01.100+08:00| -0.72| |1970-01-01T00:00:01.200+08:00| 1.34| |1970-01-01T00:00:01.300+08:00| -0.25| |1970-01-01T00:00:01.400+08:00| 0.17| |1970-01-01T00:00:01.500+08:00| 2.51| |1970-01-01T00:00:01.600+08:00| 1.42| |1970-01-01T00:00:01.700+08:00| -1.34| |1970-01-01T00:00:01.800+08:00| -0.01| |1970-01-01T00:00:01.900+08:00| -0.49| |1970-01-01T00:00:02.000+08:00| 1.63| +-----------------------------+---------------+
SQL for query:
select QLB(s1) from root.test.d1
Output series:
+-----------------------------+--------------------+ | Time|QLB(root.test.d1.s1)| +-----------------------------+--------------------+ |1970-01-01T00:00:00.001+08:00| 0.2168702295315677| |1970-01-01T00:00:00.002+08:00| 0.3068948509261751| |1970-01-01T00:00:00.003+08:00| 0.4217859150918444| |1970-01-01T00:00:00.004+08:00| 0.5114539874276656| |1970-01-01T00:00:00.005+08:00| 0.6560619525616759| |1970-01-01T00:00:00.006+08:00| 0.7722398654053280| |1970-01-01T00:00:00.007+08:00| 0.8532491661465290| |1970-01-01T00:00:00.008+08:00| 0.9028575017542528| |1970-01-01T00:00:00.009+08:00| 0.9434989988192729| |1970-01-01T00:00:00.010+08:00| 0.8950280161464689| |1970-01-01T00:00:00.011+08:00| 0.7701048398839656| |1970-01-01T00:00:00.012+08:00| 0.7845536060001281| |1970-01-01T00:00:00.013+08:00| 0.5943030981705825| |1970-01-01T00:00:00.014+08:00| 0.4618413512531093| |1970-01-01T00:00:00.015+08:00| 0.2645948244673964| |1970-01-01T00:00:00.016+08:00| 0.3167530476666645| |1970-01-01T00:00:00.017+08:00| 0.2330010780351453| |1970-01-01T00:00:00.018+08:00| 0.0666611237622325| +-----------------------------+--------------------+
create function re_sample as 'org.apache.iotdb.library.dprofile.UDTFResample'
This function is used to resample the input series according to a given frequency, including up-sampling and down-sampling. Currently, the supported up-sampling methods are NaN (filling with NaN), FFill (filling with previous value), BFill (filling with next value) and Linear (filling with linear interpolation). Down-sampling relies on group aggregation, which supports Max, Min, First, Last, Mean and Median.
Name: RESAMPLE
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
every: The frequency of resampling, which is a positive number with an unit. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. This parameter cannot be lacked.interp: The interpolation method of up-sampling, which is ‘NaN’, ‘FFill’, ‘BFill’ or ‘Linear’. By default, NaN is used.aggr: The aggregation method of down-sampling, which is ‘Max’, ‘Min’, ‘First’, ‘Last’, ‘Mean’ or ‘Median’. By default, Mean is used.start: The start time (inclusive) of resampling with the format ‘yyyy-MM-dd HH:mm:ss’. By default, it is the timestamp of the first valid data point.end: The end time (exclusive) of resampling with the format ‘yyyy-MM-dd HH:mm:ss’. By default, it is the timestamp of the last valid data point.Output Series: Output a single series. The type is DOUBLE. It is strictly equispaced with the frequency every.
Note: NaN in the input series will be ignored.
When the frequency of resampling is higher than the original frequency, up-sampling starts.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2021-03-06T16:00:00.000+08:00| 3.09| |2021-03-06T16:15:00.000+08:00| 3.53| |2021-03-06T16:30:00.000+08:00| 3.5| |2021-03-06T16:45:00.000+08:00| 3.51| |2021-03-06T17:00:00.000+08:00| 3.41| +-----------------------------+---------------+
SQL for query:
select resample(s1,'every'='5m','interp'='linear') from root.test.d1
Output series:
+-----------------------------+----------------------------------------------------------+ | Time|resample(root.test.d1.s1, "every"="5m", "interp"="linear")| +-----------------------------+----------------------------------------------------------+ |2021-03-06T16:00:00.000+08:00| 3.0899999141693115| |2021-03-06T16:05:00.000+08:00| 3.2366665999094644| |2021-03-06T16:10:00.000+08:00| 3.3833332856496177| |2021-03-06T16:15:00.000+08:00| 3.5299999713897705| |2021-03-06T16:20:00.000+08:00| 3.5199999809265137| |2021-03-06T16:25:00.000+08:00| 3.509999990463257| |2021-03-06T16:30:00.000+08:00| 3.5| |2021-03-06T16:35:00.000+08:00| 3.503333330154419| |2021-03-06T16:40:00.000+08:00| 3.506666660308838| |2021-03-06T16:45:00.000+08:00| 3.509999990463257| |2021-03-06T16:50:00.000+08:00| 3.4766666889190674| |2021-03-06T16:55:00.000+08:00| 3.443333387374878| |2021-03-06T17:00:00.000+08:00| 3.4100000858306885| +-----------------------------+----------------------------------------------------------+
When the frequency of resampling is lower than the original frequency, down-sampling starts.
Input series is the same as above, the SQL for query is shown below:
select resample(s1,'every'='30m','aggr'='first') from root.test.d1
Output series:
+-----------------------------+--------------------------------------------------------+ | Time|resample(root.test.d1.s1, "every"="30m", "aggr"="first")| +-----------------------------+--------------------------------------------------------+ |2021-03-06T16:00:00.000+08:00| 3.0899999141693115| |2021-03-06T16:30:00.000+08:00| 3.5| |2021-03-06T17:00:00.000+08:00| 3.4100000858306885| +-----------------------------+--------------------------------------------------------+
The time period of resampling can be specified with start and end. The period outside the actual time range will be interpolated.
Input series is the same as above, the SQL for query is shown below:
select resample(s1,'every'='30m','start'='2021-03-06 15:00:00') from root.test.d1
Output series:
+-----------------------------+-----------------------------------------------------------------------+ | Time|resample(root.test.d1.s1, "every"="30m", "start"="2021-03-06 15:00:00")| +-----------------------------+-----------------------------------------------------------------------+ |2021-03-06T15:00:00.000+08:00| NaN| |2021-03-06T15:30:00.000+08:00| NaN| |2021-03-06T16:00:00.000+08:00| 3.309999942779541| |2021-03-06T16:30:00.000+08:00| 3.5049999952316284| |2021-03-06T17:00:00.000+08:00| 3.4100000858306885| +-----------------------------+-----------------------------------------------------------------------+
create function sample as 'org.apache.iotdb.library.dprofile.UDTFSample'
This function is used to sample the input series, that is, select a specified number of data points from the input series and output them. Currently, three sampling methods are supported: Reservoir sampling randomly selects data points. All of the points have the same probability of being sampled. Isometric sampling selects data points at equal index intervals. Triangle sampling assigns data points to the buckets based on the number of sampling. Then it calculates the area of the triangle based on these points inside the bucket and selects the point with the largest area of the triangle. For more detail, please read paper
Name: SAMPLE
Input Series: Only support a single input series. The type is arbitrary.
Parameters:
method: The method of sampling, which is ‘reservoir’, ‘isometric’ or ‘triangle’. By default, reservoir sampling is used.k: The number of sampling, which is a positive integer. By default, it's 1.Output Series: Output a single series. The type is the same as the input. The length of the output series is k. Each data point in the output series comes from the input series.
Note: If k is greater than the length of input series, all data points in the input series will be output.
When method is ‘reservoir’ or the default, reservoir sampling is used. Due to the randomness of this method, the output series shown below is only a possible result.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:01.000+08:00| 1.0| |2020-01-01T00:00:02.000+08:00| 2.0| |2020-01-01T00:00:03.000+08:00| 3.0| |2020-01-01T00:00:04.000+08:00| 4.0| |2020-01-01T00:00:05.000+08:00| 5.0| |2020-01-01T00:00:06.000+08:00| 6.0| |2020-01-01T00:00:07.000+08:00| 7.0| |2020-01-01T00:00:08.000+08:00| 8.0| |2020-01-01T00:00:09.000+08:00| 9.0| |2020-01-01T00:00:10.000+08:00| 10.0| +-----------------------------+---------------+
SQL for query:
select sample(s1,'method'='reservoir','k'='5') from root.test.d1
Output series:
+-----------------------------+------------------------------------------------------+ | Time|sample(root.test.d1.s1, "method"="reservoir", "k"="5")| +-----------------------------+------------------------------------------------------+ |2020-01-01T00:00:02.000+08:00| 2.0| |2020-01-01T00:00:03.000+08:00| 3.0| |2020-01-01T00:00:05.000+08:00| 5.0| |2020-01-01T00:00:08.000+08:00| 8.0| |2020-01-01T00:00:10.000+08:00| 10.0| +-----------------------------+------------------------------------------------------+
When method is ‘isometric’, isometric sampling is used.
Input series is the same as above, the SQL for query is shown below:
select sample(s1,'method'='isometric','k'='5') from root.test.d1
Output series:
+-----------------------------+------------------------------------------------------+ | Time|sample(root.test.d1.s1, "method"="isometric", "k"="5")| +-----------------------------+------------------------------------------------------+ |2020-01-01T00:00:01.000+08:00| 1.0| |2020-01-01T00:00:03.000+08:00| 3.0| |2020-01-01T00:00:05.000+08:00| 5.0| |2020-01-01T00:00:07.000+08:00| 7.0| |2020-01-01T00:00:09.000+08:00| 9.0| +-----------------------------+------------------------------------------------------+
create function segment as 'org.apache.iotdb.library.dprofile.UDTFSegment'
This function is used to segment a time series into subsequences according to linear trend, and returns linear fitted values of first values in each subsequence or every data point.
Name: SEGMENT
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
output :“all” to output all fitted points; “first” to output first fitted points in each subsequence.
error: error allowed at linear regression. It is defined as mean absolute error of a subsequence.
Output Series: Output a single series. The type is DOUBLE.
Note: This function treat input series as equal-interval sampled. All data are loaded, so downsample input series first if there are too many data points.
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |1970-01-01T08:00:00.000+08:00| 5.0| |1970-01-01T08:00:00.100+08:00| 0.0| |1970-01-01T08:00:00.200+08:00| 1.0| |1970-01-01T08:00:00.300+08:00| 2.0| |1970-01-01T08:00:00.400+08:00| 3.0| |1970-01-01T08:00:00.500+08:00| 4.0| |1970-01-01T08:00:00.600+08:00| 5.0| |1970-01-01T08:00:00.700+08:00| 6.0| |1970-01-01T08:00:00.800+08:00| 7.0| |1970-01-01T08:00:00.900+08:00| 8.0| |1970-01-01T08:00:01.000+08:00| 9.0| |1970-01-01T08:00:01.100+08:00| 9.1| |1970-01-01T08:00:01.200+08:00| 9.2| |1970-01-01T08:00:01.300+08:00| 9.3| |1970-01-01T08:00:01.400+08:00| 9.4| |1970-01-01T08:00:01.500+08:00| 9.5| |1970-01-01T08:00:01.600+08:00| 9.6| |1970-01-01T08:00:01.700+08:00| 9.7| |1970-01-01T08:00:01.800+08:00| 9.8| |1970-01-01T08:00:01.900+08:00| 9.9| |1970-01-01T08:00:02.000+08:00| 10.0| |1970-01-01T08:00:02.100+08:00| 8.0| |1970-01-01T08:00:02.200+08:00| 6.0| |1970-01-01T08:00:02.300+08:00| 4.0| |1970-01-01T08:00:02.400+08:00| 2.0| |1970-01-01T08:00:02.500+08:00| 0.0| |1970-01-01T08:00:02.600+08:00| -2.0| |1970-01-01T08:00:02.700+08:00| -4.0| |1970-01-01T08:00:02.800+08:00| -6.0| |1970-01-01T08:00:02.900+08:00| -8.0| |1970-01-01T08:00:03.000+08:00| -10.0| |1970-01-01T08:00:03.100+08:00| 10.0| |1970-01-01T08:00:03.200+08:00| 10.0| |1970-01-01T08:00:03.300+08:00| 10.0| |1970-01-01T08:00:03.400+08:00| 10.0| |1970-01-01T08:00:03.500+08:00| 10.0| |1970-01-01T08:00:03.600+08:00| 10.0| |1970-01-01T08:00:03.700+08:00| 10.0| |1970-01-01T08:00:03.800+08:00| 10.0| |1970-01-01T08:00:03.900+08:00| 10.0| +-----------------------------+------------+
SQL for query:
select segment(s1, "error"="0.1") from root.test
Output series:
+-----------------------------+------------------------------------+ | Time|segment(root.test.s1, "error"="0.1")| +-----------------------------+------------------------------------+ |1970-01-01T08:00:00.000+08:00| 5.0| |1970-01-01T08:00:00.200+08:00| 1.0| |1970-01-01T08:00:01.000+08:00| 9.0| |1970-01-01T08:00:02.000+08:00| 10.0| |1970-01-01T08:00:03.000+08:00| -10.0| |1970-01-01T08:00:03.200+08:00| 10.0| +-----------------------------+------------------------------------+
create function skew as 'org.apache.iotdb.library.dprofile.UDAFSkew'
This function is used to calculate the population skewness.
Name: SKEW
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the population skewness.
Note: Missing points, null points and NaN in the input series will be ignored.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:00.000+08:00| 1.0| |2020-01-01T00:00:01.000+08:00| 2.0| |2020-01-01T00:00:02.000+08:00| 3.0| |2020-01-01T00:00:03.000+08:00| 4.0| |2020-01-01T00:00:04.000+08:00| 5.0| |2020-01-01T00:00:05.000+08:00| 6.0| |2020-01-01T00:00:06.000+08:00| 7.0| |2020-01-01T00:00:07.000+08:00| 8.0| |2020-01-01T00:00:08.000+08:00| 9.0| |2020-01-01T00:00:09.000+08:00| 10.0| |2020-01-01T00:00:10.000+08:00| 10.0| |2020-01-01T00:00:11.000+08:00| 10.0| |2020-01-01T00:00:12.000+08:00| 10.0| |2020-01-01T00:00:13.000+08:00| 10.0| |2020-01-01T00:00:14.000+08:00| 10.0| |2020-01-01T00:00:15.000+08:00| 10.0| |2020-01-01T00:00:16.000+08:00| 10.0| |2020-01-01T00:00:17.000+08:00| 10.0| |2020-01-01T00:00:18.000+08:00| 10.0| |2020-01-01T00:00:19.000+08:00| 10.0| +-----------------------------+---------------+
SQL for query:
select skew(s1) from root.test.d1
Output series:
+-----------------------------+-----------------------+ | Time| skew(root.test.d1.s1)| +-----------------------------+-----------------------+ |1970-01-01T08:00:00.000+08:00| -0.9998427402292644| +-----------------------------+-----------------------+
create function spline as 'org.apache.iotdb.library.dprofile.UDTFSpline'
This function is used to calculate cubic spline interpolation of input series.
Name: SPLINE
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
points: Number of resampling points.Output Series: Output a single series. The type is DOUBLE.
Note: Output series retains the first and last timestamps of input series. Interpolation points are selected at equal intervals. The function tries to calculate only when there are no less than 4 points in input series.
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |1970-01-01T08:00:00.000+08:00| 0.0| |1970-01-01T08:00:00.300+08:00| 1.2| |1970-01-01T08:00:00.500+08:00| 1.7| |1970-01-01T08:00:00.700+08:00| 2.0| |1970-01-01T08:00:00.900+08:00| 2.1| |1970-01-01T08:00:01.100+08:00| 2.0| |1970-01-01T08:00:01.200+08:00| 1.8| |1970-01-01T08:00:01.300+08:00| 1.2| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 1.6| +-----------------------------+------------+
SQL for query:
select spline(s1, "points"="151") from root.test
Output series:
+-----------------------------+------------------------------------+ | Time|spline(root.test.s1, "points"="151")| +-----------------------------+------------------------------------+ |1970-01-01T08:00:00.000+08:00| 0.0| |1970-01-01T08:00:00.010+08:00| 0.04870000251134237| |1970-01-01T08:00:00.020+08:00| 0.09680000495910646| |1970-01-01T08:00:00.030+08:00| 0.14430000734329226| |1970-01-01T08:00:00.040+08:00| 0.19120000966389972| |1970-01-01T08:00:00.050+08:00| 0.23750001192092896| |1970-01-01T08:00:00.060+08:00| 0.2832000141143799| |1970-01-01T08:00:00.070+08:00| 0.32830001624425253| |1970-01-01T08:00:00.080+08:00| 0.3728000183105469| |1970-01-01T08:00:00.090+08:00| 0.416700020313263| |1970-01-01T08:00:00.100+08:00| 0.4600000222524008| |1970-01-01T08:00:00.110+08:00| 0.5027000241279602| |1970-01-01T08:00:00.120+08:00| 0.5448000259399414| |1970-01-01T08:00:00.130+08:00| 0.5863000276883443| |1970-01-01T08:00:00.140+08:00| 0.627200029373169| |1970-01-01T08:00:00.150+08:00| 0.6675000309944153| |1970-01-01T08:00:00.160+08:00| 0.7072000325520833| |1970-01-01T08:00:00.170+08:00| 0.7463000340461731| |1970-01-01T08:00:00.180+08:00| 0.7848000354766846| |1970-01-01T08:00:00.190+08:00| 0.8227000368436178| |1970-01-01T08:00:00.200+08:00| 0.8600000381469728| |1970-01-01T08:00:00.210+08:00| 0.8967000393867494| |1970-01-01T08:00:00.220+08:00| 0.9328000405629477| |1970-01-01T08:00:00.230+08:00| 0.9683000416755676| |1970-01-01T08:00:00.240+08:00| 1.0032000427246095| |1970-01-01T08:00:00.250+08:00| 1.037500043710073| |1970-01-01T08:00:00.260+08:00| 1.071200044631958| |1970-01-01T08:00:00.270+08:00| 1.1043000454902647| |1970-01-01T08:00:00.280+08:00| 1.1368000462849934| |1970-01-01T08:00:00.290+08:00| 1.1687000470161437| |1970-01-01T08:00:00.300+08:00| 1.2000000476837158| |1970-01-01T08:00:00.310+08:00| 1.2307000483103594| |1970-01-01T08:00:00.320+08:00| 1.2608000489139557| |1970-01-01T08:00:00.330+08:00| 1.2903000494873524| |1970-01-01T08:00:00.340+08:00| 1.3192000500233967| |1970-01-01T08:00:00.350+08:00| 1.3475000505149364| |1970-01-01T08:00:00.360+08:00| 1.3752000509548186| |1970-01-01T08:00:00.370+08:00| 1.402300051335891| |1970-01-01T08:00:00.380+08:00| 1.4288000516510009| |1970-01-01T08:00:00.390+08:00| 1.4547000518929958| |1970-01-01T08:00:00.400+08:00| 1.480000052054723| |1970-01-01T08:00:00.410+08:00| 1.5047000521290301| |1970-01-01T08:00:00.420+08:00| 1.5288000521087646| |1970-01-01T08:00:00.430+08:00| 1.5523000519867738| |1970-01-01T08:00:00.440+08:00| 1.575200051755905| |1970-01-01T08:00:00.450+08:00| 1.597500051409006| |1970-01-01T08:00:00.460+08:00| 1.619200050938924| |1970-01-01T08:00:00.470+08:00| 1.6403000503385066| |1970-01-01T08:00:00.480+08:00| 1.660800049600601| |1970-01-01T08:00:00.490+08:00| 1.680700048718055| |1970-01-01T08:00:00.500+08:00| 1.7000000476837158| |1970-01-01T08:00:00.510+08:00| 1.7188475466453037| |1970-01-01T08:00:00.520+08:00| 1.7373800457262996| |1970-01-01T08:00:00.530+08:00| 1.7555825448831923| |1970-01-01T08:00:00.540+08:00| 1.7734400440724702| |1970-01-01T08:00:00.550+08:00| 1.790937543250622| |1970-01-01T08:00:00.560+08:00| 1.8080600423741364| |1970-01-01T08:00:00.570+08:00| 1.8247925413995016| |1970-01-01T08:00:00.580+08:00| 1.8411200402832066| |1970-01-01T08:00:00.590+08:00| 1.8570275389817397| |1970-01-01T08:00:00.600+08:00| 1.8725000374515897| |1970-01-01T08:00:00.610+08:00| 1.8875225356492449| |1970-01-01T08:00:00.620+08:00| 1.902080033531194| |1970-01-01T08:00:00.630+08:00| 1.9161575310539258| |1970-01-01T08:00:00.640+08:00| 1.9297400281739288| |1970-01-01T08:00:00.650+08:00| 1.9428125248476913| |1970-01-01T08:00:00.660+08:00| 1.9553600210317021| |1970-01-01T08:00:00.670+08:00| 1.96736751668245| |1970-01-01T08:00:00.680+08:00| 1.9788200117564232| |1970-01-01T08:00:00.690+08:00| 1.9897025062101101| |1970-01-01T08:00:00.700+08:00| 2.0| |1970-01-01T08:00:00.710+08:00| 2.0097024933913334| |1970-01-01T08:00:00.720+08:00| 2.0188199867081615| |1970-01-01T08:00:00.730+08:00| 2.027367479995188| |1970-01-01T08:00:00.740+08:00| 2.0353599732971155| |1970-01-01T08:00:00.750+08:00| 2.0428124666586482| |1970-01-01T08:00:00.760+08:00| 2.049739960124489| |1970-01-01T08:00:00.770+08:00| 2.056157453739342| |1970-01-01T08:00:00.780+08:00| 2.06207994754791| |1970-01-01T08:00:00.790+08:00| 2.067522441594897| |1970-01-01T08:00:00.800+08:00| 2.072499935925006| |1970-01-01T08:00:00.810+08:00| 2.07702743058294| |1970-01-01T08:00:00.820+08:00| 2.081119925613404| |1970-01-01T08:00:00.830+08:00| 2.0847924210611| |1970-01-01T08:00:00.840+08:00| 2.0880599169707317| |1970-01-01T08:00:00.850+08:00| 2.0909374133870027| |1970-01-01T08:00:00.860+08:00| 2.0934399103546166| |1970-01-01T08:00:00.870+08:00| 2.0955824079182768| |1970-01-01T08:00:00.880+08:00| 2.0973799061226863| |1970-01-01T08:00:00.890+08:00| 2.098847405012549| |1970-01-01T08:00:00.900+08:00| 2.0999999046325684| |1970-01-01T08:00:00.910+08:00| 2.1005574051201332| |1970-01-01T08:00:00.920+08:00| 2.1002599065303778| |1970-01-01T08:00:00.930+08:00| 2.0991524087846245| |1970-01-01T08:00:00.940+08:00| 2.0972799118041947| |1970-01-01T08:00:00.950+08:00| 2.0946874155104105| |1970-01-01T08:00:00.960+08:00| 2.0914199198245944| |1970-01-01T08:00:00.970+08:00| 2.0875224246680673| |1970-01-01T08:00:00.980+08:00| 2.083039929962151| |1970-01-01T08:00:00.990+08:00| 2.0780174356281687| |1970-01-01T08:00:01.000+08:00| 2.0724999415874406| |1970-01-01T08:00:01.010+08:00| 2.06653244776129| |1970-01-01T08:00:01.020+08:00| 2.060159954071038| |1970-01-01T08:00:01.030+08:00| 2.053427460438006| |1970-01-01T08:00:01.040+08:00| 2.046379966783517| |1970-01-01T08:00:01.050+08:00| 2.0390624730288924| |1970-01-01T08:00:01.060+08:00| 2.031519979095454| |1970-01-01T08:00:01.070+08:00| 2.0237974849045237| |1970-01-01T08:00:01.080+08:00| 2.015939990377423| |1970-01-01T08:00:01.090+08:00| 2.0079924954354746| |1970-01-01T08:00:01.100+08:00| 2.0| |1970-01-01T08:00:01.110+08:00| 1.9907018211101906| |1970-01-01T08:00:01.120+08:00| 1.9788509124245144| |1970-01-01T08:00:01.130+08:00| 1.9645127287932083| |1970-01-01T08:00:01.140+08:00| 1.9477527250665083| |1970-01-01T08:00:01.150+08:00| 1.9286363560946513| |1970-01-01T08:00:01.160+08:00| 1.9072290767278735| |1970-01-01T08:00:01.170+08:00| 1.8835963418164114| |1970-01-01T08:00:01.180+08:00| 1.8578036062105014| |1970-01-01T08:00:01.190+08:00| 1.8299163247603802| |1970-01-01T08:00:01.200+08:00| 1.7999999523162842| |1970-01-01T08:00:01.210+08:00| 1.7623635841923329| |1970-01-01T08:00:01.220+08:00| 1.7129696477516976| |1970-01-01T08:00:01.230+08:00| 1.6543635959181928| |1970-01-01T08:00:01.240+08:00| 1.5890908816156328| |1970-01-01T08:00:01.250+08:00| 1.5196969577678319| |1970-01-01T08:00:01.260+08:00| 1.4487272772986044| |1970-01-01T08:00:01.270+08:00| 1.3787272931317647| |1970-01-01T08:00:01.280+08:00| 1.3122424581911272| |1970-01-01T08:00:01.290+08:00| 1.251818225400506| |1970-01-01T08:00:01.300+08:00| 1.2000000476837158| |1970-01-01T08:00:01.310+08:00| 1.1548000470995912| |1970-01-01T08:00:01.320+08:00| 1.1130667107899999| |1970-01-01T08:00:01.330+08:00| 1.0756000393033045| |1970-01-01T08:00:01.340+08:00| 1.043200033187868| |1970-01-01T08:00:01.350+08:00| 1.016666692992053| |1970-01-01T08:00:01.360+08:00| 0.9968000192642223| |1970-01-01T08:00:01.370+08:00| 0.9844000125527389| |1970-01-01T08:00:01.380+08:00| 0.9802666734059655| |1970-01-01T08:00:01.390+08:00| 0.9852000023722649| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.410+08:00| 1.023999999165535| |1970-01-01T08:00:01.420+08:00| 1.0559999990463256| |1970-01-01T08:00:01.430+08:00| 1.0959999996423722| |1970-01-01T08:00:01.440+08:00| 1.1440000009536744| |1970-01-01T08:00:01.450+08:00| 1.2000000029802322| |1970-01-01T08:00:01.460+08:00| 1.264000005722046| |1970-01-01T08:00:01.470+08:00| 1.3360000091791153| |1970-01-01T08:00:01.480+08:00| 1.4160000133514405| |1970-01-01T08:00:01.490+08:00| 1.5040000182390214| |1970-01-01T08:00:01.500+08:00| 1.600000023841858| +-----------------------------+------------------------------------+
create function spread as 'org.apache.iotdb.library.dprofile.UDAFSpread'
This function is used to calculate the spread of time series, that is, the maximum value minus the minimum value.
Name: SPREAD
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series. The type is the same as the input. There is only one data point in the series, whose timestamp is 0 and value is the spread.
Note: Missing points, null points and NaN in the input series will be ignored.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| +-----------------------------+---------------+
SQL for query:
select spread(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
Output series:
+-----------------------------+-----------------------+ | Time|spread(root.test.d1.s1)| +-----------------------------+-----------------------+ |1970-01-01T08:00:00.000+08:00| 26.0| +-----------------------------+-----------------------+
create function zscore as 'org.apache.iotdb.library.dprofile.UDTFZScore'
This function is used to standardize the input series with z-score.
Name: ZSCORE
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
compute: When set to “batch”, anomaly test is conducted after importing all data points; when set to “stream”, it is required to provide mean and standard deviation. The default method is “batch”.avg: Mean value when method is set to “stream”.sd: Standard deviation when method is set to “stream”.Output Series: Output a single series. The type is DOUBLE.
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |1970-01-01T08:00:00.100+08:00| 0.0| |1970-01-01T08:00:00.200+08:00| 0.0| |1970-01-01T08:00:00.300+08:00| 1.0| |1970-01-01T08:00:00.400+08:00| -1.0| |1970-01-01T08:00:00.500+08:00| 0.0| |1970-01-01T08:00:00.600+08:00| 0.0| |1970-01-01T08:00:00.700+08:00| -2.0| |1970-01-01T08:00:00.800+08:00| 2.0| |1970-01-01T08:00:00.900+08:00| 0.0| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 1.0| |1970-01-01T08:00:01.200+08:00| -1.0| |1970-01-01T08:00:01.300+08:00| -1.0| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 0.0| |1970-01-01T08:00:01.600+08:00| 0.0| |1970-01-01T08:00:01.700+08:00| 10.0| |1970-01-01T08:00:01.800+08:00| 2.0| |1970-01-01T08:00:01.900+08:00| -2.0| |1970-01-01T08:00:02.000+08:00| 0.0| +-----------------------------+------------+
SQL for query:
select zscore(s1) from root.test
Output series:
+-----------------------------+--------------------+ | Time|zscore(root.test.s1)| +-----------------------------+--------------------+ |1970-01-01T08:00:00.100+08:00|-0.20672455764868078| |1970-01-01T08:00:00.200+08:00|-0.20672455764868078| |1970-01-01T08:00:00.300+08:00| 0.20672455764868078| |1970-01-01T08:00:00.400+08:00| -0.6201736729460423| |1970-01-01T08:00:00.500+08:00|-0.20672455764868078| |1970-01-01T08:00:00.600+08:00|-0.20672455764868078| |1970-01-01T08:00:00.700+08:00| -1.033622788243404| |1970-01-01T08:00:00.800+08:00| 0.6201736729460423| |1970-01-01T08:00:00.900+08:00|-0.20672455764868078| |1970-01-01T08:00:01.000+08:00|-0.20672455764868078| |1970-01-01T08:00:01.100+08:00| 0.20672455764868078| |1970-01-01T08:00:01.200+08:00| -0.6201736729460423| |1970-01-01T08:00:01.300+08:00| -0.6201736729460423| |1970-01-01T08:00:01.400+08:00| 0.20672455764868078| |1970-01-01T08:00:01.500+08:00|-0.20672455764868078| |1970-01-01T08:00:01.600+08:00|-0.20672455764868078| |1970-01-01T08:00:01.700+08:00| 3.9277665953249348| |1970-01-01T08:00:01.800+08:00| 0.6201736729460423| |1970-01-01T08:00:01.900+08:00| -1.033622788243404| |1970-01-01T08:00:02.000+08:00|-0.20672455764868078| +-----------------------------+--------------------+
create function iqr as 'org.apache.iotdb.library.anomaly.UDTFIQR'
This function is used to detect anomalies based on IQR. Points distributing beyond 1.5 times IQR are selected.
Name: IQR
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
method: When set to “batch”, anomaly test is conducted after importing all data points; when set to “stream”, it is required to provide upper and lower quantiles. The default method is “batch”.q1: The lower quantile when method is set to “stream”.q3: The upper quantile when method is set to “stream”.Output Series: Output a single series. The type is DOUBLE.
Note: $IQR=Q_3-Q_1$
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |1970-01-01T08:00:00.100+08:00| 0.0| |1970-01-01T08:00:00.200+08:00| 0.0| |1970-01-01T08:00:00.300+08:00| 1.0| |1970-01-01T08:00:00.400+08:00| -1.0| |1970-01-01T08:00:00.500+08:00| 0.0| |1970-01-01T08:00:00.600+08:00| 0.0| |1970-01-01T08:00:00.700+08:00| -2.0| |1970-01-01T08:00:00.800+08:00| 2.0| |1970-01-01T08:00:00.900+08:00| 0.0| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 1.0| |1970-01-01T08:00:01.200+08:00| -1.0| |1970-01-01T08:00:01.300+08:00| -1.0| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 0.0| |1970-01-01T08:00:01.600+08:00| 0.0| |1970-01-01T08:00:01.700+08:00| 10.0| |1970-01-01T08:00:01.800+08:00| 2.0| |1970-01-01T08:00:01.900+08:00| -2.0| |1970-01-01T08:00:02.000+08:00| 0.0| +-----------------------------+------------+
SQL for query:
select iqr(s1) from root.test
Output series:
+-----------------------------+-----------------+ | Time|iqr(root.test.s1)| +-----------------------------+-----------------+ |1970-01-01T08:00:01.700+08:00| 10.0| +-----------------------------+-----------------+
create function ksigma as 'org.apache.iotdb.library.anomaly.UDTFKSigma'
This function is used to detect anomalies based on the Dynamic K-Sigma Algorithm. Within a sliding window, the input value with a deviation of more than k times the standard deviation from the average will be output as anomaly.
Name: KSIGMA
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
k: How many times to multiply on standard deviation to define anomaly, the default value is 3.window: The window size of Dynamic K-Sigma Algorithm, the default value is 10000.Output Series: Output a single series. The type is same as input series.
Note: Only when is larger than 0, the anomaly detection will be performed. Otherwise, nothing will be output.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 0.0| |2020-01-01T00:00:03.000+08:00| 50.0| |2020-01-01T00:00:04.000+08:00| 100.0| |2020-01-01T00:00:06.000+08:00| 150.0| |2020-01-01T00:00:08.000+08:00| 200.0| |2020-01-01T00:00:10.000+08:00| 200.0| |2020-01-01T00:00:14.000+08:00| 200.0| |2020-01-01T00:00:15.000+08:00| 200.0| |2020-01-01T00:00:16.000+08:00| 200.0| |2020-01-01T00:00:18.000+08:00| 200.0| |2020-01-01T00:00:20.000+08:00| 150.0| |2020-01-01T00:00:22.000+08:00| 100.0| |2020-01-01T00:00:26.000+08:00| 50.0| |2020-01-01T00:00:28.000+08:00| 0.0| |2020-01-01T00:00:30.000+08:00| NaN| +-----------------------------+---------------+
SQL for query:
select ksigma(s1,"k"="1.0") from root.test.d1 where time <= 2020-01-01 00:00:30
Output series:
+-----------------------------+---------------------------------+ |Time |ksigma(root.test.d1.s1,"k"="3.0")| +-----------------------------+---------------------------------+ |2020-01-01T00:00:02.000+08:00| 0.0| |2020-01-01T00:00:03.000+08:00| 50.0| |2020-01-01T00:00:26.000+08:00| 50.0| |2020-01-01T00:00:28.000+08:00| 0.0| +-----------------------------+---------------------------------+
create function LOF as 'org.apache.iotdb.library.anomaly.UDTFLOF'
This function is used to detect density anomaly of time series. According to k-th distance calculation parameter and local outlier factor (lof) threshold, the function judges if a set of input values is an density anomaly, and a bool mark of anomaly values will be output.
Name: LOF
Input Series: Multiple input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
method:assign a detection method. The default value is “default”, when input data has multiple dimensions. The alternative is “series”, when a input series will be transformed to high dimension.k:use the k-th distance to calculate lof. Default value is 3.window: size of window to split origin data points. Default value is 10000.windowsize:dimension that will be transformed into when method is “series”. The default value is 5.Output Series: Output a single series. The type is DOUBLE.
Note: Incomplete rows will be ignored. They are neither calculated nor marked as anomaly.
Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d1.s1|root.test.d1.s2| +-----------------------------+---------------+---------------+ |1970-01-01T08:00:00.100+08:00| 0.0| 0.0| |1970-01-01T08:00:00.200+08:00| 0.0| 1.0| |1970-01-01T08:00:00.300+08:00| 1.0| 1.0| |1970-01-01T08:00:00.400+08:00| 1.0| 0.0| |1970-01-01T08:00:00.500+08:00| 0.0| -1.0| |1970-01-01T08:00:00.600+08:00| -1.0| -1.0| |1970-01-01T08:00:00.700+08:00| -1.0| 0.0| |1970-01-01T08:00:00.800+08:00| 2.0| 2.0| |1970-01-01T08:00:00.900+08:00| 0.0| null| +-----------------------------+---------------+---------------+
SQL for query:
select lof(s1,s2) from root.test.d1 where time<1000
Output series:
+-----------------------------+-------------------------------------+ | Time|lof(root.test.d1.s1, root.test.d1.s2)| +-----------------------------+-------------------------------------+ |1970-01-01T08:00:00.100+08:00| 3.8274824267668244| |1970-01-01T08:00:00.200+08:00| 3.0117631741126156| |1970-01-01T08:00:00.300+08:00| 2.838155437762879| |1970-01-01T08:00:00.400+08:00| 3.0117631741126156| |1970-01-01T08:00:00.500+08:00| 2.73518261244453| |1970-01-01T08:00:00.600+08:00| 2.371440975708148| |1970-01-01T08:00:00.700+08:00| 2.73518261244453| |1970-01-01T08:00:00.800+08:00| 1.7561416374270742| +-----------------------------+-------------------------------------+
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |1970-01-01T08:00:00.100+08:00| 1.0| |1970-01-01T08:00:00.200+08:00| 2.0| |1970-01-01T08:00:00.300+08:00| 3.0| |1970-01-01T08:00:00.400+08:00| 4.0| |1970-01-01T08:00:00.500+08:00| 5.0| |1970-01-01T08:00:00.600+08:00| 6.0| |1970-01-01T08:00:00.700+08:00| 7.0| |1970-01-01T08:00:00.800+08:00| 8.0| |1970-01-01T08:00:00.900+08:00| 9.0| |1970-01-01T08:00:01.000+08:00| 10.0| |1970-01-01T08:00:01.100+08:00| 11.0| |1970-01-01T08:00:01.200+08:00| 12.0| |1970-01-01T08:00:01.300+08:00| 13.0| |1970-01-01T08:00:01.400+08:00| 14.0| |1970-01-01T08:00:01.500+08:00| 15.0| |1970-01-01T08:00:01.600+08:00| 16.0| |1970-01-01T08:00:01.700+08:00| 17.0| |1970-01-01T08:00:01.800+08:00| 18.0| |1970-01-01T08:00:01.900+08:00| 19.0| |1970-01-01T08:00:02.000+08:00| 20.0| +-----------------------------+---------------+
SQL for query:
select lof(s1, "method"="series") from root.test.d1 where time<1000
Output series:
+-----------------------------+--------------------+ | Time|lof(root.test.d1.s1)| +-----------------------------+--------------------+ |1970-01-01T08:00:00.100+08:00| 3.77777777777778| |1970-01-01T08:00:00.200+08:00| 4.32727272727273| |1970-01-01T08:00:00.300+08:00| 4.85714285714286| |1970-01-01T08:00:00.400+08:00| 5.40909090909091| |1970-01-01T08:00:00.500+08:00| 5.94999999999999| |1970-01-01T08:00:00.600+08:00| 6.43243243243243| |1970-01-01T08:00:00.700+08:00| 6.79999999999999| |1970-01-01T08:00:00.800+08:00| 7.0| |1970-01-01T08:00:00.900+08:00| 7.0| |1970-01-01T08:00:01.000+08:00| 6.79999999999999| |1970-01-01T08:00:01.100+08:00| 6.43243243243243| |1970-01-01T08:00:01.200+08:00| 5.94999999999999| |1970-01-01T08:00:01.300+08:00| 5.40909090909091| |1970-01-01T08:00:01.400+08:00| 4.85714285714286| |1970-01-01T08:00:01.500+08:00| 4.32727272727273| |1970-01-01T08:00:01.600+08:00| 3.77777777777778| +-----------------------------+--------------------+
create function missdetect as 'org.apache.iotdb.library.anomaly.UDTFMissDetect'
This function is used to detect missing anomalies. In some datasets, missing values are filled by linear interpolation. Thus, there are several long perfect linear segments. By discovering these perfect linear segments, missing anomalies are detected.
Name: MISSDETECT
Input Series: Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
Parameter:
error: The minimum length of the detected missing anomalies, which is an integer greater than or equal to 10. By default, it is 10.
Output Series: Output a single series. The type is BOOLEAN. Each data point which is miss anomaly will be labeled as true.
Input series:
+-----------------------------+---------------+ | Time|root.test.d2.s2| +-----------------------------+---------------+ |2021-07-01T12:00:00.000+08:00| 0.0| |2021-07-01T12:00:01.000+08:00| 1.0| |2021-07-01T12:00:02.000+08:00| 0.0| |2021-07-01T12:00:03.000+08:00| 1.0| |2021-07-01T12:00:04.000+08:00| 0.0| |2021-07-01T12:00:05.000+08:00| 0.0| |2021-07-01T12:00:06.000+08:00| 0.0| |2021-07-01T12:00:07.000+08:00| 0.0| |2021-07-01T12:00:08.000+08:00| 0.0| |2021-07-01T12:00:09.000+08:00| 0.0| |2021-07-01T12:00:10.000+08:00| 0.0| |2021-07-01T12:00:11.000+08:00| 0.0| |2021-07-01T12:00:12.000+08:00| 0.0| |2021-07-01T12:00:13.000+08:00| 0.0| |2021-07-01T12:00:14.000+08:00| 0.0| |2021-07-01T12:00:15.000+08:00| 0.0| |2021-07-01T12:00:16.000+08:00| 1.0| |2021-07-01T12:00:17.000+08:00| 0.0| |2021-07-01T12:00:18.000+08:00| 1.0| |2021-07-01T12:00:19.000+08:00| 0.0| |2021-07-01T12:00:20.000+08:00| 1.0| +-----------------------------+---------------+
SQL for query:
select missdetect(s2,'minlen'='10') from root.test.d2
Output series:
+-----------------------------+------------------------------------------+ | Time|missdetect(root.test.d2.s2, "minlen"="10")| +-----------------------------+------------------------------------------+ |2021-07-01T12:00:00.000+08:00| false| |2021-07-01T12:00:01.000+08:00| false| |2021-07-01T12:00:02.000+08:00| false| |2021-07-01T12:00:03.000+08:00| false| |2021-07-01T12:00:04.000+08:00| true| |2021-07-01T12:00:05.000+08:00| true| |2021-07-01T12:00:06.000+08:00| true| |2021-07-01T12:00:07.000+08:00| true| |2021-07-01T12:00:08.000+08:00| true| |2021-07-01T12:00:09.000+08:00| true| |2021-07-01T12:00:10.000+08:00| true| |2021-07-01T12:00:11.000+08:00| true| |2021-07-01T12:00:12.000+08:00| true| |2021-07-01T12:00:13.000+08:00| true| |2021-07-01T12:00:14.000+08:00| true| |2021-07-01T12:00:15.000+08:00| true| |2021-07-01T12:00:16.000+08:00| false| |2021-07-01T12:00:17.000+08:00| false| |2021-07-01T12:00:18.000+08:00| false| |2021-07-01T12:00:19.000+08:00| false| |2021-07-01T12:00:20.000+08:00| false| +-----------------------------+------------------------------------------+
create function range as 'org.apache.iotdb.library.anomaly.UDTFRange'
This function is used to detect range anomaly of time series. According to upper bound and lower bound parameters, the function judges if a input value is beyond range, aka range anomaly, and a new time series of anomaly will be output.
Name: RANGE
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
lower_bound:lower bound of range anomaly detection.upper_bound:upper bound of range anomaly detection.Output Series: Output a single series. The type is the same as the input.
Note: Only when upper_bound is larger than lower_bound, the anomaly detection will be performed. Otherwise, nothing will be output.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| +-----------------------------+---------------+
SQL for query:
select range(s1,"lower_bound"="101.0","upper_bound"="125.0") from root.test.d1 where time <= 2020-01-01 00:00:30
Output series:
+-----------------------------+------------------------------------------------------------------+ |Time |range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")| +-----------------------------+------------------------------------------------------------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:28.000+08:00| 126.0| +-----------------------------+------------------------------------------------------------------+
create function twosidedfilter as 'org.apache.iotdb.library.anomaly.UDTFTwoSidedFilter'
The function is used to filter anomalies of a numeric time series based on two-sided window detection.
Name: TWOSIDEDFILTER
Input Series: Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE
Output Series: Output a single series. The type is the same as the input. It is the input without anomalies.
Parameter:
len: The size of the window, which is a positive integer. By default, it's 5. When len=3, the algorithm detects forward window and backward window with length 3 and calculates the outlierness of the current point.
threshold: The threshold of outlierness, which is a floating number in (0,1). By default, it's 0.3. The strict standard of detecting anomalies is in proportion to the threshold.
Input series:
+-----------------------------+------------+ | Time|root.test.s0| +-----------------------------+------------+ |1970-01-01T08:00:00.000+08:00| 2002.0| |1970-01-01T08:00:01.000+08:00| 1946.0| |1970-01-01T08:00:02.000+08:00| 1958.0| |1970-01-01T08:00:03.000+08:00| 2012.0| |1970-01-01T08:00:04.000+08:00| 2051.0| |1970-01-01T08:00:05.000+08:00| 1898.0| |1970-01-01T08:00:06.000+08:00| 2014.0| |1970-01-01T08:00:07.000+08:00| 2052.0| |1970-01-01T08:00:08.000+08:00| 1935.0| |1970-01-01T08:00:09.000+08:00| 1901.0| |1970-01-01T08:00:10.000+08:00| 1972.0| |1970-01-01T08:00:11.000+08:00| 1969.0| |1970-01-01T08:00:12.000+08:00| 1984.0| |1970-01-01T08:00:13.000+08:00| 2018.0| |1970-01-01T08:00:37.000+08:00| 1484.0| |1970-01-01T08:00:38.000+08:00| 1055.0| |1970-01-01T08:00:39.000+08:00| 1050.0| |1970-01-01T08:01:05.000+08:00| 1023.0| |1970-01-01T08:01:06.000+08:00| 1056.0| |1970-01-01T08:01:07.000+08:00| 978.0| |1970-01-01T08:01:08.000+08:00| 1050.0| |1970-01-01T08:01:09.000+08:00| 1123.0| |1970-01-01T08:01:10.000+08:00| 1150.0| |1970-01-01T08:01:11.000+08:00| 1034.0| |1970-01-01T08:01:12.000+08:00| 950.0| |1970-01-01T08:01:13.000+08:00| 1059.0| +-----------------------------+------------+
SQL for query:
select TwoSidedFilter(s0, 'len'='5', 'threshold'='0.3') from root.test
Output series:
+-----------------------------+------------+ | Time|root.test.s0| +-----------------------------+------------+ |1970-01-01T08:00:00.000+08:00| 2002.0| |1970-01-01T08:00:01.000+08:00| 1946.0| |1970-01-01T08:00:02.000+08:00| 1958.0| |1970-01-01T08:00:03.000+08:00| 2012.0| |1970-01-01T08:00:04.000+08:00| 2051.0| |1970-01-01T08:00:05.000+08:00| 1898.0| |1970-01-01T08:00:06.000+08:00| 2014.0| |1970-01-01T08:00:07.000+08:00| 2052.0| |1970-01-01T08:00:08.000+08:00| 1935.0| |1970-01-01T08:00:09.000+08:00| 1901.0| |1970-01-01T08:00:10.000+08:00| 1972.0| |1970-01-01T08:00:11.000+08:00| 1969.0| |1970-01-01T08:00:12.000+08:00| 1984.0| |1970-01-01T08:00:13.000+08:00| 2018.0| |1970-01-01T08:01:05.000+08:00| 1023.0| |1970-01-01T08:01:06.000+08:00| 1056.0| |1970-01-01T08:01:07.000+08:00| 978.0| |1970-01-01T08:01:08.000+08:00| 1050.0| |1970-01-01T08:01:09.000+08:00| 1123.0| |1970-01-01T08:01:10.000+08:00| 1150.0| |1970-01-01T08:01:11.000+08:00| 1034.0| |1970-01-01T08:01:12.000+08:00| 950.0| |1970-01-01T08:01:13.000+08:00| 1059.0| +-----------------------------+------------+
create function outlier as 'org.apache.iotdb.library.anomaly.UDTFOutlier'
This function is used to detect distance-based outliers. For each point in the current window, if the number of its neighbors within the distance of neighbor distance threshold is less than the neighbor count threshold, the point in detected as an outlier.
Name: OUTLIER
Input Series： Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
r：the neighbor distance threshold.k：the neighbor count threshold.w：the window size.s：the slide size.Output Series: Output a single series. The type is the same as the input.
Input series:
+-----------------------------+------------+ | Time|root.test.s1| +-----------------------------+------------+ |2020-01-04T23:59:55.000+08:00| 56.0| |2020-01-04T23:59:56.000+08:00| 55.1| |2020-01-04T23:59:57.000+08:00| 54.2| |2020-01-04T23:59:58.000+08:00| 56.3| |2020-01-04T23:59:59.000+08:00| 59.0| |2020-01-05T00:00:00.000+08:00| 60.0| |2020-01-05T00:00:01.000+08:00| 60.5| |2020-01-05T00:00:02.000+08:00| 64.5| |2020-01-05T00:00:03.000+08:00| 69.0| |2020-01-05T00:00:04.000+08:00| 64.2| |2020-01-05T00:00:05.000+08:00| 62.3| |2020-01-05T00:00:06.000+08:00| 58.0| |2020-01-05T00:00:07.000+08:00| 58.9| |2020-01-05T00:00:08.000+08:00| 52.0| |2020-01-05T00:00:09.000+08:00| 62.3| |2020-01-05T00:00:10.000+08:00| 61.0| |2020-01-05T00:00:11.000+08:00| 64.2| |2020-01-05T00:00:12.000+08:00| 61.8| |2020-01-05T00:00:13.000+08:00| 64.0| |2020-01-05T00:00:14.000+08:00| 63.0| +-----------------------------+------------+
SQL for query:
select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
Output series:
+-----------------------------+--------------------------------------------------------+ | Time|outlier(root.test.s1,"r"="5.0","k"="4","w"="10","s"="5")| +-----------------------------+--------------------------------------------------------+ |2020-01-05T00:00:03.000+08:00| 69.0| +-----------------------------+--------------------------------------------------------+ |2020-01-05T00:00:08.000+08:00| 52.0| +-----------------------------+--------------------------------------------------------+
This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
Name: MasterTrain
Input Series： Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
p: The order of the model.eta: The distance threshold. By default, it will be estimated based on the 3-sigma rule.Output Series: Output a single series. The type is the same as the input.
Installation
research/master-detector.mvn spotless:apply.mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies../library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar to ./ext/udf/.create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain' in client.Input series:
+-----------------------------+------------+------------+--------------+--------------+ | Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| +-----------------------------+------------+------------+--------------+--------------+ |1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| |1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| |1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| |1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| |1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| |1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| |1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| |1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014| |1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| |1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| |1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| |1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| |1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| |1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| |1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| |1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| |1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| |1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| |1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| |1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| |1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| |1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| |1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| |1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| |1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| +-----------------------------+------------+------------+--------------+--------------+
SQL for query:
select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
Output series:
+-----------------------------+---------------------------------------------------------------------------------------------+ | Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")| +-----------------------------+---------------------------------------------------------------------------------------------+ |1970-01-01T08:00:00.001+08:00| 0.13656607660463288| |1970-01-01T08:00:00.002+08:00| 0.8291884323013894| |1970-01-01T08:00:00.003+08:00| 0.05012816073171693| |1970-01-01T08:00:00.004+08:00| -0.5495287787485761| |1970-01-01T08:00:00.005+08:00| 0.03740486307345578| |1970-01-01T08:00:00.006+08:00| 1.0500132150475212| |1970-01-01T08:00:00.007+08:00| 0.04583944643116993| |1970-01-01T08:00:00.008+08:00| -0.07863708480736269| +-----------------------------+---------------------------------------------------------------------------------------------+
This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
Name: MasterDetect
Input Series： Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
p: The order of the model.k: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.eta: The distance threshold. By default, it will be estimated based on the 3-sigma rule.eta: The detection threshold. By default, it will be estimated based on the 3-sigma rule.output_type: The type of output. ‘repair’ for repairing and ‘anomaly’ for anomaly detection.output_column: The repaired column to output, defaults to 1 which means output the repair result of the first column.Output Series: Output a single series. The type is the same as the input.
Installation
research/master-detector.mvn spotless:apply.mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies../library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar to ./ext/udf/.create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect' in client.Input series:
+-----------------------------+------------+------------+--------------+--------------+--------------------+ | Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model| +-----------------------------+------------+------------+--------------+--------------+--------------------+ |1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288| |1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894| |1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693| |1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761| |1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578| |1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212| |1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993| |1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269| |1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null| |1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null| |1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null| |1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null| |1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null| |1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null| |1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null| |1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null| |1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null| |1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null| |1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null| |1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null| |1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null| |1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null| |1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null| |1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null| |1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null| +-----------------------------+------------+------------+--------------+--------------+--------------------+
SQL for query:
select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
Output series:
+-----------------------------+--------------------------------------------------------------------------------------+ | Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')| +-----------------------------+--------------------------------------------------------------------------------------+ |1970-01-01T08:00:00.001+08:00| 116.327274| |1970-01-01T08:00:00.002+08:00| 116.327305| |1970-01-01T08:00:00.003+08:00| 116.3273291| |1970-01-01T08:00:00.004+08:00| 116.327342| |1970-01-01T08:00:00.005+08:00| 116.3273744| |1970-01-01T08:00:00.006+08:00| 116.3274117| |1970-01-01T08:00:00.007+08:00| 116.3274396| |1970-01-01T08:00:00.008+08:00| 116.3274668| |1970-01-01T08:00:00.009+08:00| 116.3275026| |1970-01-01T08:00:00.010+08:00| 116.3274967| |1970-01-01T08:00:00.011+08:00| 116.3274929| |1970-01-01T08:00:00.012+08:00| 116.3274745| |1970-01-01T08:00:00.013+08:00| 116.3275095| |1970-01-01T08:00:00.014+08:00| 116.3274787| |1970-01-01T08:00:00.015+08:00| 116.3274693| |1970-01-01T08:00:00.016+08:00| 116.3274941| |1970-01-01T08:00:00.017+08:00| 116.3275401| |1970-01-01T08:00:00.018+08:00| 116.3275713| |1970-01-01T08:00:00.019+08:00| 116.3276003| |1970-01-01T08:00:00.020+08:00| 116.3276308| |1970-01-01T08:00:00.021+08:00| 116.3276338| |1970-01-01T08:00:00.022+08:00| 116.3276684| |1970-01-01T08:00:00.023+08:00| 116.3277016| |1970-01-01T08:00:00.024+08:00| 116.3277284| |1970-01-01T08:00:00.025+08:00| 116.3277562| +-----------------------------+--------------------------------------------------------------------------------------+
SQL for query:
select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
Output series:
+-----------------------------+---------------------------------------------------------------------------------------+ | Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')| +-----------------------------+---------------------------------------------------------------------------------------+ |1970-01-01T08:00:00.001+08:00| false| |1970-01-01T08:00:00.002+08:00| false| |1970-01-01T08:00:00.003+08:00| false| |1970-01-01T08:00:00.004+08:00| false| |1970-01-01T08:00:00.005+08:00| true| |1970-01-01T08:00:00.006+08:00| true| |1970-01-01T08:00:00.007+08:00| false| |1970-01-01T08:00:00.008+08:00| false| |1970-01-01T08:00:00.009+08:00| false| |1970-01-01T08:00:00.010+08:00| false| |1970-01-01T08:00:00.011+08:00| false| |1970-01-01T08:00:00.012+08:00| false| |1970-01-01T08:00:00.013+08:00| false| |1970-01-01T08:00:00.014+08:00| true| |1970-01-01T08:00:00.015+08:00| false| |1970-01-01T08:00:00.016+08:00| false| |1970-01-01T08:00:00.017+08:00| false| |1970-01-01T08:00:00.018+08:00| false| |1970-01-01T08:00:00.019+08:00| false| |1970-01-01T08:00:00.020+08:00| false| |1970-01-01T08:00:00.021+08:00| false| |1970-01-01T08:00:00.022+08:00| false| |1970-01-01T08:00:00.023+08:00| false| |1970-01-01T08:00:00.024+08:00| false| |1970-01-01T08:00:00.025+08:00| false| +-----------------------------+---------------------------------------------------------------------------------------+
create function conv as 'org.apache.iotdb.library.frequency.UDTFConv'
This function is used to calculate the convolution, i.e. polynomial multiplication.
Name: CONV
Input: Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE.
Output: Output a single series. The type is DOUBLE. It is the result of convolution whose timestamps starting from 0 only indicate the order.
Note: NaN in the input series will be ignored.
Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d2.s1|root.test.d2.s2| +-----------------------------+---------------+---------------+ |1970-01-01T08:00:00.000+08:00| 1.0| 7.0| |1970-01-01T08:00:00.001+08:00| 0.0| 2.0| |1970-01-01T08:00:00.002+08:00| 1.0| null| +-----------------------------+---------------+---------------+
SQL for query:
select conv(s1,s2) from root.test.d2
Output series:
+-----------------------------+--------------------------------------+ | Time|conv(root.test.d2.s1, root.test.d2.s2)| +-----------------------------+--------------------------------------+ |1970-01-01T08:00:00.000+08:00| 7.0| |1970-01-01T08:00:00.001+08:00| 2.0| |1970-01-01T08:00:00.002+08:00| 7.0| |1970-01-01T08:00:00.003+08:00| 2.0| +-----------------------------+--------------------------------------+
create function deconv as 'org.apache.iotdb.library.frequency.UDTFDeconv'
This function is used to calculate the deconvolution, i.e. polynomial division.
Name: DECONV
Input: Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
result: The result of deconvolution, which is ‘quotient’ or ‘remainder’. By default, the quotient will be output.Output: Output a single series. The type is DOUBLE. It is the result of deconvolving the second series from the first series (dividing the first series by the second series) whose timestamps starting from 0 only indicate the order.
Note: NaN in the input series will be ignored.
When result is ‘quotient’ or the default, this function calculates the quotient of the deconvolution.
Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d2.s3|root.test.d2.s2| +-----------------------------+---------------+---------------+ |1970-01-01T08:00:00.000+08:00| 8.0| 7.0| |1970-01-01T08:00:00.001+08:00| 2.0| 2.0| |1970-01-01T08:00:00.002+08:00| 7.0| null| |1970-01-01T08:00:00.003+08:00| 2.0| null| +-----------------------------+---------------+---------------+
SQL for query:
select deconv(s3,s2) from root.test.d2
Output series:
+-----------------------------+----------------------------------------+ | Time|deconv(root.test.d2.s3, root.test.d2.s2)| +-----------------------------+----------------------------------------+ |1970-01-01T08:00:00.000+08:00| 1.0| |1970-01-01T08:00:00.001+08:00| 0.0| |1970-01-01T08:00:00.002+08:00| 1.0| +-----------------------------+----------------------------------------+
When result is ‘remainder’, this function calculates the remainder of the deconvolution.
Input series is the same as above, the SQL for query is shown below:
select deconv(s3,s2,'result'='remainder') from root.test.d2
Output series:
+-----------------------------+--------------------------------------------------------------+ | Time|deconv(root.test.d2.s3, root.test.d2.s2, "result"="remainder")| +-----------------------------+--------------------------------------------------------------+ |1970-01-01T08:00:00.000+08:00| 1.0| |1970-01-01T08:00:00.001+08:00| 0.0| |1970-01-01T08:00:00.002+08:00| 0.0| |1970-01-01T08:00:00.003+08:00| 0.0| +-----------------------------+--------------------------------------------------------------+
create function dwt as 'org.apache.iotdb.library.frequency.UDTFDWT'
This function is used to calculate 1d discrete wavelet transform of a numerical series.
Name: DWT
Input: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
method: The type of wavelet. May select ‘Haar’, ‘DB4’, ‘DB6’, ‘DB8’, where DB means Daubechies. User may offer coefficients of wavelet transform and ignore this parameter. Case ignored.coef: Coefficients of wavelet transform. When providing this parameter, use comma ‘,’ to split them, and leave no spaces or other punctuations.layer: Times to transform. The number of output vectors equals $layer+1$. Default is 1.Output: Output a single series. The type is DOUBLE. The length is the same as the input.
Note: The length of input series must be an integer number power of 2.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |1970-01-01T08:00:00.000+08:00| 0.0| |1970-01-01T08:00:00.100+08:00| 0.2| |1970-01-01T08:00:00.200+08:00| 1.5| |1970-01-01T08:00:00.300+08:00| 1.2| |1970-01-01T08:00:00.400+08:00| 0.6| |1970-01-01T08:00:00.500+08:00| 1.7| |1970-01-01T08:00:00.600+08:00| 0.8| |1970-01-01T08:00:00.700+08:00| 2.0| |1970-01-01T08:00:00.800+08:00| 2.5| |1970-01-01T08:00:00.900+08:00| 2.1| |1970-01-01T08:00:01.000+08:00| 0.0| |1970-01-01T08:00:01.100+08:00| 2.0| |1970-01-01T08:00:01.200+08:00| 1.8| |1970-01-01T08:00:01.300+08:00| 1.2| |1970-01-01T08:00:01.400+08:00| 1.0| |1970-01-01T08:00:01.500+08:00| 1.6| +-----------------------------+---------------+
SQL for query:
select dwt(s1,"method"="haar") from root.test.d1
Output series:
+-----------------------------+-------------------------------------+ | Time|dwt(root.test.d1.s1, "method"="haar")| +-----------------------------+-------------------------------------+ |1970-01-01T08:00:00.000+08:00| 0.14142135834465192| |1970-01-01T08:00:00.100+08:00| 1.909188342921157| |1970-01-01T08:00:00.200+08:00| 1.6263456473052773| |1970-01-01T08:00:00.300+08:00| 1.9798989957517026| |1970-01-01T08:00:00.400+08:00| 3.252691126023161| |1970-01-01T08:00:00.500+08:00| 1.414213562373095| |1970-01-01T08:00:00.600+08:00| 2.1213203435596424| |1970-01-01T08:00:00.700+08:00| 1.8384776479437628| |1970-01-01T08:00:00.800+08:00| -0.14142135834465192| |1970-01-01T08:00:00.900+08:00| 0.21213200063848547| |1970-01-01T08:00:01.000+08:00| -0.7778174761639416| |1970-01-01T08:00:01.100+08:00| -0.8485281289944873| |1970-01-01T08:00:01.200+08:00| 0.2828427799095765| |1970-01-01T08:00:01.300+08:00| -1.414213562373095| |1970-01-01T08:00:01.400+08:00| 0.42426400127697095| |1970-01-01T08:00:01.500+08:00| -0.42426408557066786| +-----------------------------+-------------------------------------+
create function fft as 'org.apache.iotdb.library.frequency.UDTFFFT'
This function is used to calculate the fast Fourier transform (FFT) of a numerical series.
Name: FFT
Input: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
method: The type of FFT, which is ‘uniform’ (by default) or ‘nonuniform’. If the value is ‘uniform’, the timestamps will be ignored and all data points will be regarded as equidistant. Thus, the equidistant fast Fourier transform algorithm will be applied. If the value is ‘nonuniform’ (TODO), the non-equidistant fast Fourier transform algorithm will be applied based on timestamps.result: The result of FFT, which is ‘real’, ‘imag’, ‘abs’ or ‘angle’, corresponding to the real part, imaginary part, magnitude and phase angle. By default, the magnitude will be output.compress: The parameter of compression, which is within (0,1]. It is the reserved energy ratio of lossy compression. By default, there is no compression.Output: Output a single series. The type is DOUBLE. The length is the same as the input. The timestamps starting from 0 only indicate the order.
Note: NaN in the input series will be ignored.
With the default type, uniform FFT is applied.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |1970-01-01T08:00:00.000+08:00| 2.902113| |1970-01-01T08:00:01.000+08:00| 1.1755705| |1970-01-01T08:00:02.000+08:00| -2.1755705| |1970-01-01T08:00:03.000+08:00| -1.9021131| |1970-01-01T08:00:04.000+08:00| 1.0| |1970-01-01T08:00:05.000+08:00| 1.9021131| |1970-01-01T08:00:06.000+08:00| 0.1755705| |1970-01-01T08:00:07.000+08:00| -1.1755705| |1970-01-01T08:00:08.000+08:00| -0.902113| |1970-01-01T08:00:09.000+08:00| 0.0| |1970-01-01T08:00:10.000+08:00| 0.902113| |1970-01-01T08:00:11.000+08:00| 1.1755705| |1970-01-01T08:00:12.000+08:00| -0.1755705| |1970-01-01T08:00:13.000+08:00| -1.9021131| |1970-01-01T08:00:14.000+08:00| -1.0| |1970-01-01T08:00:15.000+08:00| 1.9021131| |1970-01-01T08:00:16.000+08:00| 2.1755705| |1970-01-01T08:00:17.000+08:00| -1.1755705| |1970-01-01T08:00:18.000+08:00| -2.902113| |1970-01-01T08:00:19.000+08:00| 0.0| +-----------------------------+---------------+
SQL for query:
select fft(s1) from root.test.d1
Output series:
+-----------------------------+----------------------+ | Time| fft(root.test.d1.s1)| +-----------------------------+----------------------+ |1970-01-01T08:00:00.000+08:00| 0.0| |1970-01-01T08:00:00.001+08:00| 1.2727111142703152E-8| |1970-01-01T08:00:00.002+08:00| 2.385520799101839E-7| |1970-01-01T08:00:00.003+08:00| 8.723291723972645E-8| |1970-01-01T08:00:00.004+08:00| 19.999999960195904| |1970-01-01T08:00:00.005+08:00| 9.999999850988388| |1970-01-01T08:00:00.006+08:00| 3.2260694930700566E-7| |1970-01-01T08:00:00.007+08:00| 8.723291605373329E-8| |1970-01-01T08:00:00.008+08:00| 1.108657103979944E-7| |1970-01-01T08:00:00.009+08:00| 1.2727110997246171E-8| |1970-01-01T08:00:00.010+08:00|1.9852334701272664E-23| |1970-01-01T08:00:00.011+08:00| 1.2727111194499847E-8| |1970-01-01T08:00:00.012+08:00| 1.108657103979944E-7| |1970-01-01T08:00:00.013+08:00| 8.723291785769131E-8| |1970-01-01T08:00:00.014+08:00| 3.226069493070057E-7| |1970-01-01T08:00:00.015+08:00| 9.999999850988388| |1970-01-01T08:00:00.016+08:00| 19.999999960195904| |1970-01-01T08:00:00.017+08:00| 8.723291747109068E-8| |1970-01-01T08:00:00.018+08:00| 2.3855207991018386E-7| |1970-01-01T08:00:00.019+08:00| 1.2727112069910878E-8| +-----------------------------+----------------------+
Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, there are peaks in $k=4$ and $k=5$ of the output.
Input series is the same as above, the SQL for query is shown below:
select fft(s1, 'result'='real', 'compress'='0.99'), fft(s1, 'result'='imag','compress'='0.99') from root.test.d1
Output series:
+-----------------------------+----------------------+----------------------+ | Time| fft(root.test.d1.s1,| fft(root.test.d1.s1,| | | "result"="real",| "result"="imag",| | | "compress"="0.99")| "compress"="0.99")| +-----------------------------+----------------------+----------------------+ |1970-01-01T08:00:00.000+08:00| 0.0| 0.0| |1970-01-01T08:00:00.001+08:00| -3.932894010461041E-9| 1.2104201863039066E-8| |1970-01-01T08:00:00.002+08:00|-1.4021739447490164E-7| 1.9299268669082926E-7| |1970-01-01T08:00:00.003+08:00| -7.057291240286645E-8| 5.127422242345858E-8| |1970-01-01T08:00:00.004+08:00| 19.021130288047125| -6.180339875198807| |1970-01-01T08:00:00.005+08:00| 9.999999850988388| 3.501852745067114E-16| |1970-01-01T08:00:00.019+08:00| -3.932894898639461E-9|-1.2104202549376264E-8| +-----------------------------+----------------------+----------------------+
Note: Based on the conjugation of the Fourier transform result, only the first half of the compression result is reserved. According to the given parameter, data points are reserved from low frequency to high frequency until the reserved energy ratio exceeds it. The last data point is reserved to indicate the length of the series.
create function highpass as 'org.apache.iotdb.library.frequency.UDTFHighPass'
This function performs low-pass filtering on the input series and extracts components above the cutoff frequency. The timestamps of input will be ignored and all data points will be regarded as equidistant.
Name: HIGHPASS
Input: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
wpass: The normalized cutoff frequency which values (0,1). This parameter cannot be lacked.Output: Output a single series. The type is DOUBLE. It is the input after filtering. The length and timestamps of output are the same as the input.
Note: NaN in the input series will be ignored.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |1970-01-01T08:00:00.000+08:00| 2.902113| |1970-01-01T08:00:01.000+08:00| 1.1755705| |1970-01-01T08:00:02.000+08:00| -2.1755705| |1970-01-01T08:00:03.000+08:00| -1.9021131| |1970-01-01T08:00:04.000+08:00| 1.0| |1970-01-01T08:00:05.000+08:00| 1.9021131| |1970-01-01T08:00:06.000+08:00| 0.1755705| |1970-01-01T08:00:07.000+08:00| -1.1755705| |1970-01-01T08:00:08.000+08:00| -0.902113| |1970-01-01T08:00:09.000+08:00| 0.0| |1970-01-01T08:00:10.000+08:00| 0.902113| |1970-01-01T08:00:11.000+08:00| 1.1755705| |1970-01-01T08:00:12.000+08:00| -0.1755705| |1970-01-01T08:00:13.000+08:00| -1.9021131| |1970-01-01T08:00:14.000+08:00| -1.0| |1970-01-01T08:00:15.000+08:00| 1.9021131| |1970-01-01T08:00:16.000+08:00| 2.1755705| |1970-01-01T08:00:17.000+08:00| -1.1755705| |1970-01-01T08:00:18.000+08:00| -2.902113| |1970-01-01T08:00:19.000+08:00| 0.0| +-----------------------------+---------------+
SQL for query:
select highpass(s1,'wpass'='0.45') from root.test.d1
Output series:
+-----------------------------+-----------------------------------------+ | Time|highpass(root.test.d1.s1, "wpass"="0.45")| +-----------------------------+-----------------------------------------+ |1970-01-01T08:00:00.000+08:00| 0.9999999534830373| |1970-01-01T08:00:01.000+08:00| 1.7462829277628608E-8| |1970-01-01T08:00:02.000+08:00| -0.9999999593178128| |1970-01-01T08:00:03.000+08:00| -4.1115269056426626E-8| |1970-01-01T08:00:04.000+08:00| 0.9999999925494194| |1970-01-01T08:00:05.000+08:00| 3.328126513330016E-8| |1970-01-01T08:00:06.000+08:00| -1.0000000183304454| |1970-01-01T08:00:07.000+08:00| 6.260191433311374E-10| |1970-01-01T08:00:08.000+08:00| 1.0000000018134796| |1970-01-01T08:00:09.000+08:00| -3.097210911744423E-17| |1970-01-01T08:00:10.000+08:00| -1.0000000018134794| |1970-01-01T08:00:11.000+08:00| -6.260191627862097E-10| |1970-01-01T08:00:12.000+08:00| 1.0000000183304454| |1970-01-01T08:00:13.000+08:00| -3.328126501424346E-8| |1970-01-01T08:00:14.000+08:00| -0.9999999925494196| |1970-01-01T08:00:15.000+08:00| 4.111526915498874E-8| |1970-01-01T08:00:16.000+08:00| 0.9999999593178128| |1970-01-01T08:00:17.000+08:00| -1.7462829341296528E-8| |1970-01-01T08:00:18.000+08:00| -0.9999999534830369| |1970-01-01T08:00:19.000+08:00| -1.035237222742873E-16| +-----------------------------+-----------------------------------------+
Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, the output is $y=sin(2\pi t/4)$ after high-pass filtering.
create function ifft as 'org.apache.iotdb.library.frequency.UDTFIFFT'
This function treats the two input series as the real and imaginary part of a complex series, performs an inverse fast Fourier transform (IFFT), and outputs the real part of the result. For the input format, please refer to the output format of FFT function. Moreover, the compressed output of FFT function is also supported.
Name: IFFT
Input: Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
start: The start time of the output series with the format ‘yyyy-MM-dd HH:mm:ss’. By default, it is ‘1970-01-01 08:00:00’.interval: The interval of the output series, which is a positive number with an unit. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. By default, it is 1s.Output: Output a single series. The type is DOUBLE. It is strictly equispaced. The values are the results of IFFT.
Note: If a row contains null points or NaN, it will be ignored.
Input series:
+-----------------------------+----------------------+----------------------+ | Time| root.test.d1.re| root.test.d1.im| +-----------------------------+----------------------+----------------------+ |1970-01-01T08:00:00.000+08:00| 0.0| 0.0| |1970-01-01T08:00:00.001+08:00| -3.932894010461041E-9| 1.2104201863039066E-8| |1970-01-01T08:00:00.002+08:00|-1.4021739447490164E-7| 1.9299268669082926E-7| |1970-01-01T08:00:00.003+08:00| -7.057291240286645E-8| 5.127422242345858E-8| |1970-01-01T08:00:00.004+08:00| 19.021130288047125| -6.180339875198807| |1970-01-01T08:00:00.005+08:00| 9.999999850988388| 3.501852745067114E-16| |1970-01-01T08:00:00.019+08:00| -3.932894898639461E-9|-1.2104202549376264E-8| +-----------------------------+----------------------+----------------------+
SQL for query:
select ifft(re, im, 'interval'='1m', 'start'='2021-01-01 00:00:00') from root.test.d1
Output series:
+-----------------------------+-------------------------------------------------------+ | Time|ifft(root.test.d1.re, root.test.d1.im, "interval"="1m",| | | "start"="2021-01-01 00:00:00")| +-----------------------------+-------------------------------------------------------+ |2021-01-01T00:00:00.000+08:00| 2.902112992431231| |2021-01-01T00:01:00.000+08:00| 1.1755704705132448| |2021-01-01T00:02:00.000+08:00| -2.175570513757101| |2021-01-01T00:03:00.000+08:00| -1.9021130389094498| |2021-01-01T00:04:00.000+08:00| 0.9999999925494194| |2021-01-01T00:05:00.000+08:00| 1.902113046743454| |2021-01-01T00:06:00.000+08:00| 0.17557053610884188| |2021-01-01T00:07:00.000+08:00| -1.1755704886020932| |2021-01-01T00:08:00.000+08:00| -0.9021130371347148| |2021-01-01T00:09:00.000+08:00| 3.552713678800501E-16| |2021-01-01T00:10:00.000+08:00| 0.9021130371347154| |2021-01-01T00:11:00.000+08:00| 1.1755704886020932| |2021-01-01T00:12:00.000+08:00| -0.17557053610884144| |2021-01-01T00:13:00.000+08:00| -1.902113046743454| |2021-01-01T00:14:00.000+08:00| -0.9999999925494196| |2021-01-01T00:15:00.000+08:00| 1.9021130389094498| |2021-01-01T00:16:00.000+08:00| 2.1755705137571004| |2021-01-01T00:17:00.000+08:00| -1.1755704705132448| |2021-01-01T00:18:00.000+08:00| -2.902112992431231| |2021-01-01T00:19:00.000+08:00| -3.552713678800501E-16| +-----------------------------+-------------------------------------------------------+
create function lowpass as 'org.apache.iotdb.library.frequency.UDTFLowPass'
This function performs low-pass filtering on the input series and extracts components below the cutoff frequency. The timestamps of input will be ignored and all data points will be regarded as equidistant.
Name: LOWPASS
Input: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
wpass: The normalized cutoff frequency which values (0,1). This parameter cannot be lacked.Output: Output a single series. The type is DOUBLE. It is the input after filtering. The length and timestamps of output are the same as the input.
Note: NaN in the input series will be ignored.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s1| +-----------------------------+---------------+ |1970-01-01T08:00:00.000+08:00| 2.902113| |1970-01-01T08:00:01.000+08:00| 1.1755705| |1970-01-01T08:00:02.000+08:00| -2.1755705| |1970-01-01T08:00:03.000+08:00| -1.9021131| |1970-01-01T08:00:04.000+08:00| 1.0| |1970-01-01T08:00:05.000+08:00| 1.9021131| |1970-01-01T08:00:06.000+08:00| 0.1755705| |1970-01-01T08:00:07.000+08:00| -1.1755705| |1970-01-01T08:00:08.000+08:00| -0.902113| |1970-01-01T08:00:09.000+08:00| 0.0| |1970-01-01T08:00:10.000+08:00| 0.902113| |1970-01-01T08:00:11.000+08:00| 1.1755705| |1970-01-01T08:00:12.000+08:00| -0.1755705| |1970-01-01T08:00:13.000+08:00| -1.9021131| |1970-01-01T08:00:14.000+08:00| -1.0| |1970-01-01T08:00:15.000+08:00| 1.9021131| |1970-01-01T08:00:16.000+08:00| 2.1755705| |1970-01-01T08:00:17.000+08:00| -1.1755705| |1970-01-01T08:00:18.000+08:00| -2.902113| |1970-01-01T08:00:19.000+08:00| 0.0| +-----------------------------+---------------+
SQL for query:
select lowpass(s1,'wpass'='0.45') from root.test.d1
Output series:
+-----------------------------+----------------------------------------+ | Time|lowpass(root.test.d1.s1, "wpass"="0.45")| +-----------------------------+----------------------------------------+ |1970-01-01T08:00:00.000+08:00| 1.9021130073323922| |1970-01-01T08:00:01.000+08:00| 1.1755704705132448| |1970-01-01T08:00:02.000+08:00| -1.1755705286582614| |1970-01-01T08:00:03.000+08:00| -1.9021130389094498| |1970-01-01T08:00:04.000+08:00| 7.450580419288145E-9| |1970-01-01T08:00:05.000+08:00| 1.902113046743454| |1970-01-01T08:00:06.000+08:00| 1.1755705212076808| |1970-01-01T08:00:07.000+08:00| -1.1755704886020932| |1970-01-01T08:00:08.000+08:00| -1.9021130222335536| |1970-01-01T08:00:09.000+08:00| 3.552713678800501E-16| |1970-01-01T08:00:10.000+08:00| 1.9021130222335536| |1970-01-01T08:00:11.000+08:00| 1.1755704886020932| |1970-01-01T08:00:12.000+08:00| -1.1755705212076801| |1970-01-01T08:00:13.000+08:00| -1.902113046743454| |1970-01-01T08:00:14.000+08:00| -7.45058112983088E-9| |1970-01-01T08:00:15.000+08:00| 1.9021130389094498| |1970-01-01T08:00:16.000+08:00| 1.1755705286582616| |1970-01-01T08:00:17.000+08:00| -1.1755704705132448| |1970-01-01T08:00:18.000+08:00| -1.9021130073323924| |1970-01-01T08:00:19.000+08:00| -2.664535259100376E-16| +-----------------------------+----------------------------------------+
Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, the output is $y=2sin(2\pi t/5)$ after low-pass filtering.
create function cov as 'org.apache.iotdb.library.dmatch.UDAFCov'
This function is used to calculate the population covariance.
Name: COV
Input Series: Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the population covariance.
Note:
NaN, it will be ignored;NaN will be output.Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d2.s1|root.test.d2.s2| +-----------------------------+---------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| 101.0| |2020-01-01T00:00:03.000+08:00| 101.0| null| |2020-01-01T00:00:04.000+08:00| 102.0| 101.0| |2020-01-01T00:00:06.000+08:00| 104.0| 102.0| |2020-01-01T00:00:08.000+08:00| 126.0| 102.0| |2020-01-01T00:00:10.000+08:00| 108.0| 103.0| |2020-01-01T00:00:12.000+08:00| null| 103.0| |2020-01-01T00:00:14.000+08:00| 112.0| 104.0| |2020-01-01T00:00:15.000+08:00| 113.0| null| |2020-01-01T00:00:16.000+08:00| 114.0| 104.0| |2020-01-01T00:00:18.000+08:00| 116.0| 105.0| |2020-01-01T00:00:20.000+08:00| 118.0| 105.0| |2020-01-01T00:00:22.000+08:00| 100.0| 106.0| |2020-01-01T00:00:26.000+08:00| 124.0| 108.0| |2020-01-01T00:00:28.000+08:00| 126.0| 108.0| |2020-01-01T00:00:30.000+08:00| NaN| 108.0| +-----------------------------+---------------+---------------+
SQL for query:
select cov(s1,s2) from root.test.d2
Output series:
+-----------------------------+-------------------------------------+ | Time|cov(root.test.d2.s1, root.test.d2.s2)| +-----------------------------+-------------------------------------+ |1970-01-01T08:00:00.000+08:00| 12.291666666666666| +-----------------------------+-------------------------------------+
create function dtw as 'org.apache.iotdb.library.dmatch.UDAFDtw'
This function is used to calculate the DTW distance between two input series.
Name: DTW
Input Series: Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the DTW distance.
Note:
NaN, it will be ignored;0 will be output.Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d2.s1|root.test.d2.s2| +-----------------------------+---------------+---------------+ |1970-01-01T08:00:00.001+08:00| 1.0| 2.0| |1970-01-01T08:00:00.002+08:00| 1.0| 2.0| |1970-01-01T08:00:00.003+08:00| 1.0| 2.0| |1970-01-01T08:00:00.004+08:00| 1.0| 2.0| |1970-01-01T08:00:00.005+08:00| 1.0| 2.0| |1970-01-01T08:00:00.006+08:00| 1.0| 2.0| |1970-01-01T08:00:00.007+08:00| 1.0| 2.0| |1970-01-01T08:00:00.008+08:00| 1.0| 2.0| |1970-01-01T08:00:00.009+08:00| 1.0| 2.0| |1970-01-01T08:00:00.010+08:00| 1.0| 2.0| |1970-01-01T08:00:00.011+08:00| 1.0| 2.0| |1970-01-01T08:00:00.012+08:00| 1.0| 2.0| |1970-01-01T08:00:00.013+08:00| 1.0| 2.0| |1970-01-01T08:00:00.014+08:00| 1.0| 2.0| |1970-01-01T08:00:00.015+08:00| 1.0| 2.0| |1970-01-01T08:00:00.016+08:00| 1.0| 2.0| |1970-01-01T08:00:00.017+08:00| 1.0| 2.0| |1970-01-01T08:00:00.018+08:00| 1.0| 2.0| |1970-01-01T08:00:00.019+08:00| 1.0| 2.0| |1970-01-01T08:00:00.020+08:00| 1.0| 2.0| +-----------------------------+---------------+---------------+
SQL for query:
select dtw(s1,s2) from root.test.d2
Output series:
+-----------------------------+-------------------------------------+ | Time|dtw(root.test.d2.s1, root.test.d2.s2)| +-----------------------------+-------------------------------------+ |1970-01-01T08:00:00.000+08:00| 20.0| +-----------------------------+-------------------------------------+
create function pearson as 'org.apache.iotdb.library.dmatch.UDAFPearson'
This function is used to calculate the Pearson Correlation Coefficient.
Name: PEARSON
Input Series: Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the Pearson Correlation Coefficient.
Note:
NaN, it will be ignored;NaN will be output.Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d2.s1|root.test.d2.s2| +-----------------------------+---------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| 101.0| |2020-01-01T00:00:03.000+08:00| 101.0| null| |2020-01-01T00:00:04.000+08:00| 102.0| 101.0| |2020-01-01T00:00:06.000+08:00| 104.0| 102.0| |2020-01-01T00:00:08.000+08:00| 126.0| 102.0| |2020-01-01T00:00:10.000+08:00| 108.0| 103.0| |2020-01-01T00:00:12.000+08:00| null| 103.0| |2020-01-01T00:00:14.000+08:00| 112.0| 104.0| |2020-01-01T00:00:15.000+08:00| 113.0| null| |2020-01-01T00:00:16.000+08:00| 114.0| 104.0| |2020-01-01T00:00:18.000+08:00| 116.0| 105.0| |2020-01-01T00:00:20.000+08:00| 118.0| 105.0| |2020-01-01T00:00:22.000+08:00| 100.0| 106.0| |2020-01-01T00:00:26.000+08:00| 124.0| 108.0| |2020-01-01T00:00:28.000+08:00| 126.0| 108.0| |2020-01-01T00:00:30.000+08:00| NaN| 108.0| +-----------------------------+---------------+---------------+
SQL for query:
select pearson(s1,s2) from root.test.d2
Output series:
+-----------------------------+-----------------------------------------+ | Time|pearson(root.test.d2.s1, root.test.d2.s2)| +-----------------------------+-----------------------------------------+ |1970-01-01T08:00:00.000+08:00| 0.5630881927754872| +-----------------------------+-----------------------------------------+
create function ptnsym as 'org.apache.iotdb.library.dmatch.UDTFPtnSym'
This function is used to find all symmetric subseries in the input whose degree of symmetry is less than the threshold. The degree of symmetry is calculated by DTW. The smaller the degree, the more symmetrical the series is.
Name: PATTERNSYMMETRIC
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE
Parameter:
window: The length of the symmetric subseries. It's a positive integer and the default value is 10.threshold: The threshold of the degree of symmetry. It's non-negative. Only the subseries whose degree of symmetry is below it will be output. By default, all subseries will be output.Output Series: Output a single series. The type is DOUBLE. Each data point in the output series corresponds to a symmetric subseries. The output timestamp is the starting timestamp of the subseries and the output value is the degree of symmetry.
Input series:
+-----------------------------+---------------+ | Time|root.test.d1.s4| +-----------------------------+---------------+ |2021-01-01T12:00:00.000+08:00| 1.0| |2021-01-01T12:00:01.000+08:00| 2.0| |2021-01-01T12:00:02.000+08:00| 3.0| |2021-01-01T12:00:03.000+08:00| 2.0| |2021-01-01T12:00:04.000+08:00| 1.0| |2021-01-01T12:00:05.000+08:00| 1.0| |2021-01-01T12:00:06.000+08:00| 1.0| |2021-01-01T12:00:07.000+08:00| 1.0| |2021-01-01T12:00:08.000+08:00| 2.0| |2021-01-01T12:00:09.000+08:00| 3.0| |2021-01-01T12:00:10.000+08:00| 2.0| |2021-01-01T12:00:11.000+08:00| 1.0| +-----------------------------+---------------+
SQL for query:
select ptnsym(s4, 'window'='5', 'threshold'='0') from root.test.d1
Output series:
+-----------------------------+------------------------------------------------------+ | Time|ptnsym(root.test.d1.s4, "window"="5", "threshold"="0")| +-----------------------------+------------------------------------------------------+ |2021-01-01T12:00:00.000+08:00| 0.0| |2021-01-01T12:00:07.000+08:00| 0.0| +-----------------------------+------------------------------------------------------+
create function xcorr as 'org.apache.iotdb.library.dmatch.UDTFXCorr'
This function is used to calculate the cross correlation function of given two time series. For discrete time series, cross correlation is given by $$CR(n) = \frac{1}{N} \sum_{m=1}^N S_1[m]S_2[m+n]$$ which represent the similarities between two series with different index shifts.
Name: XCORR
Input Series: Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Output Series: Output a single series with DOUBLE as datatype. There are $2N-1$ data points in the series, the center of which represents the cross correlation calculated with pre-aligned series(that is $CR(0)$ in the formula above), and the previous(or post) values represent those with shifting the latter series forward(or backward otherwise) until the two series are no longer overlapped(not included). In short, the values of output series are given by(index starts from 1) $$OS[i] = CR(-N+i) = \frac{1}{N} \sum_{m=1}^{i} S_1[m]S_2[N-i+m],\ if\ i <= N$$ $$OS[i] = CR(i-N) = \frac{1}{N} \sum_{m=1}^{2N-i} S_1[i-N+m]S_2[m],\ if\ i > N$$
Note:
null and NaN values in the input series will be ignored and treated as 0.Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d1.s1|root.test.d1.s2| +-----------------------------+---------------+---------------+ |2020-01-01T00:00:01.000+08:00| null| 6| |2020-01-01T00:00:02.000+08:00| 2| 7| |2020-01-01T00:00:03.000+08:00| 3| NaN| |2020-01-01T00:00:04.000+08:00| 4| 9| |2020-01-01T00:00:05.000+08:00| 5| 10| +-----------------------------+---------------+---------------+
SQL for query:
select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05
Output series:
+-----------------------------+---------------------------------------+ | Time|xcorr(root.test.d1.s1, root.test.d1.s2)| +-----------------------------+---------------------------------------+ |1970-01-01T08:00:00.001+08:00| 0.0| |1970-01-01T08:00:00.002+08:00| 4.0| |1970-01-01T08:00:00.003+08:00| 9.6| |1970-01-01T08:00:00.004+08:00| 13.4| |1970-01-01T08:00:00.005+08:00| 20.0| |1970-01-01T08:00:00.006+08:00| 15.6| |1970-01-01T08:00:00.007+08:00| 9.2| |1970-01-01T08:00:00.008+08:00| 11.8| |1970-01-01T08:00:00.009+08:00| 6.0| +-----------------------------+---------------------------------------+
create function pattern_match as 'org.apache.iotdb.library.match.UDAFPatternMatch'
This function performs pattern matching between an input time series and a predefined pattern. A match is considered successful if the similarity measure (distance) is less than or equal to a specified threshold. The results are output as a JSON list.
Name: PATTERN_MATCH
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE/ BOOLEAN
Parameter:
timePattern : A comma-separated string of timestamps (e.g., "t1,t2,t3"). Length must be greater than 1. Required.valuePattern : A comma-separated string of numerical values corresponding to timePattern. Length must match timePattern and be greater than 1. Required.For boolean values: Use
1fortrueand0forfalse.
theshold: Float-type similarity threshold. Required.Output Series: A JSON list containing all successfully matched segments. Each entry includes: start timestamp startTime, end timestamp endTime, calculated similarity value distance.
Input series:
IoTDB> select s0 from root.** +-----------------------------+-------------+ | Time|root.db.d0.s0| +-----------------------------+-------------+ |1970-01-01T08:00:00.001+08:00| 0.0| |1970-01-01T08:00:00.002+08:00| 1.1| |1970-01-01T08:00:00.003+08:00| 1.2| |1970-01-01T08:00:00.004+08:00| 1.3| |1970-01-01T08:00:00.005+08:00| 0.0| +-----------------------------+-------------+
SQL for query:
select pattern_match (s0, "timePattern"="1,2,3", "valuePattern"="1.1,1.2,1.3", "threshold"="0.5") as match_result from root.db.d0
Output series:
+--------------------------------------------------------------------------------------------------+ | match_result| +--------------------------------------------------------------------------------------------------+ |[{"distance":0.200000,"startTime":1,"endTime":3}, {"distance":0.000000,"startTime":2,"endTime":4}]| +--------------------------------------------------------------------------------------------------+
Input series:
IoTDB> select s1 from root.** +-----------------------------+-------------+ | Time|root.db.d0.s1| +-----------------------------+-------------+ |1970-01-01T08:00:00.001+08:00| true| |1970-01-01T08:00:00.002+08:00| true| |1970-01-01T08:00:00.003+08:00| true| |1970-01-01T08:00:00.004+08:00| false| |1970-01-01T08:00:00.005+08:00| false| +-----------------------------+-------------+
SQL for query:
select pattern_match (s1, "timePattern"="1,2,3", "valuePattern"="1,1,1", "threshold"="0.5") as match_result from root.db.d0
Output series:
+-------------------------------------------------+ | match_result| +-------------------------------------------------+ |[{"distance":0.000000,"startTime":1,"endTime":3}]| +-------------------------------------------------+
Input series:
IoTDB> select s2 from root.** +-----------------------------+-------------+ | Time|root.db.d0.s2| +-----------------------------+-------------+ |1970-01-01T08:00:00.001+08:00| 0.0| |1970-01-01T08:00:00.002+08:00| -1.0| |1970-01-01T08:00:00.003+08:00| -2.0| |1970-01-01T08:00:00.004+08:00| -3.0| |1970-01-01T08:00:00.005+08:00| -2.0| |1970-01-01T08:00:00.006+08:00| -1.0| |1970-01-01T08:00:00.007+08:00| -0.0| |1970-01-01T08:00:00.008+08:00| -0.0| |1970-01-01T08:00:00.009+08:00| -0.0| |1970-01-01T08:00:00.010+08:00| -0.0| +-----------------------------+-------------+
SQL for query:
select pattern_match (s2, "timePattern"="1,2,3,4,5,6,7", "valuePattern"="0.0,-1.0,-2.0,-3.0,-2.0,-1.0,-0.0", "threshold"="10") as match_result from root.db.d0
Output series:
+----------------------------------------------+ | match_result| +----------------------------------------------+ |[{"distance":0.53,"startTime":1,"endTime":10}]| +----------------------------------------------+
Input series:
IoTDB> select s0,s1 from root.** +-----------------------------+-------------+-------------+ | Time|root.db.d0.s0|root.db.d0.s1| +-----------------------------+-------------+-------------+ |1970-01-01T08:00:00.001+08:00| 0.0| true| |1970-01-01T08:00:00.002+08:00| 1.1| true| |1970-01-01T08:00:00.003+08:00| 1.2| true| |1970-01-01T08:00:00.004+08:00| 1.3| false| |1970-01-01T08:00:00.005+08:00| 0.0| false| +-----------------------------+-------------+-------------+
SQL for query:
select pattern_match (s0, "timePattern"="1,2,3", "valuePattern"="1.1,1.2,1.3", "threshold"="0.5") as match_result1, pattern_match (s1, "timePattern"="1,2,3", "valuePattern"="1,1,1", "threshold"="0.5") as match_result2 from root.db.d0
Output series:
+--------------------------------------------------------------------------------------------------+-------------------------------------------------+ | match_result1| match_result2| +--------------------------------------------------------------------------------------------------+-------------------------------------------------+ |[{"distance":0.200000,"startTime":1,"endTime":3}, {"distance":0.000000,"startTime":2,"endTime":4}]|[{"distance":0.000000,"startTime":1,"endTime":3}]| +--------------------------------------------------------------------------------------------------+-------------------------------------------------+
create function timestamprepair as 'org.apache.iotdb.library.drepair.UDTFTimestampRepair'
This function is used for timestamp repair. According to the given standard time interval, the method of minimizing the repair cost is adopted. By fine-tuning the timestamps, the original data with unstable timestamp interval is repaired to strictly equispaced data. If no standard time interval is given, this function will use the median, mode or cluster of the time interval to estimate the standard time interval.
Name: TIMESTAMPREPAIR
Input Series: Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
interval: The standard time interval whose unit is millisecond. It is a positive integer. By default, it will be estimated according to the given method.method: The method to estimate the standard time interval, which is ‘median’, ‘mode’ or ‘cluster’. This parameter is only valid when interval is not given. By default, median will be used.Output Series: Output a single series. The type is the same as the input. This series is the input after repairing.
When interval is given, this function repairs according to the given standard time interval.
Input series:
+-----------------------------+---------------+ | Time|root.test.d2.s1| +-----------------------------+---------------+ |2021-07-01T12:00:00.000+08:00| 1.0| |2021-07-01T12:00:10.000+08:00| 2.0| |2021-07-01T12:00:19.000+08:00| 3.0| |2021-07-01T12:00:30.000+08:00| 4.0| |2021-07-01T12:00:40.000+08:00| 5.0| |2021-07-01T12:00:50.000+08:00| 6.0| |2021-07-01T12:01:01.000+08:00| 7.0| |2021-07-01T12:01:11.000+08:00| 8.0| |2021-07-01T12:01:21.000+08:00| 9.0| |2021-07-01T12:01:31.000+08:00| 10.0| +-----------------------------+---------------+
SQL for query:
select timestamprepair(s1,'interval'='10000') from root.test.d2
Output series:
+-----------------------------+----------------------------------------------------+ | Time|timestamprepair(root.test.d2.s1, "interval"="10000")| +-----------------------------+----------------------------------------------------+ |2021-07-01T12:00:00.000+08:00| 1.0| |2021-07-01T12:00:10.000+08:00| 2.0| |2021-07-01T12:00:20.000+08:00| 3.0| |2021-07-01T12:00:30.000+08:00| 4.0| |2021-07-01T12:00:40.000+08:00| 5.0| |2021-07-01T12:00:50.000+08:00| 6.0| |2021-07-01T12:01:00.000+08:00| 7.0| |2021-07-01T12:01:10.000+08:00| 8.0| |2021-07-01T12:01:20.000+08:00| 9.0| |2021-07-01T12:01:30.000+08:00| 10.0| +-----------------------------+----------------------------------------------------+
When interval is default, this function estimates the standard time interval.
Input series is the same as above, the SQL for query is shown below:
select timestamprepair(s1) from root.test.d2
Output series:
+-----------------------------+--------------------------------+ | Time|timestamprepair(root.test.d2.s1)| +-----------------------------+--------------------------------+ |2021-07-01T12:00:00.000+08:00| 1.0| |2021-07-01T12:00:10.000+08:00| 2.0| |2021-07-01T12:00:20.000+08:00| 3.0| |2021-07-01T12:00:30.000+08:00| 4.0| |2021-07-01T12:00:40.000+08:00| 5.0| |2021-07-01T12:00:50.000+08:00| 6.0| |2021-07-01T12:01:00.000+08:00| 7.0| |2021-07-01T12:01:10.000+08:00| 8.0| |2021-07-01T12:01:20.000+08:00| 9.0| |2021-07-01T12:01:30.000+08:00| 10.0| +-----------------------------+--------------------------------+
create function valuefill as 'org.apache.iotdb.library.drepair.UDTFValueFill'
This function is used to impute time series. Several methods are supported.
Name: ValueFill Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
method: {“mean”, “previous”, “linear”, “likelihood”, “AR”, “MA”, “SCREEN”}, default “linear”. Method to use for imputation in series. “mean”: use global mean value to fill holes; “previous”: propagate last valid observation forward to next valid. “linear”: simplest interpolation method; “likelihood”:Maximum likelihood estimation based on the normal distribution of speed; “AR”: auto regression; “MA”: moving average; “SCREEN”: speed constraint.Output Series: Output a single series. The type is the same as the input. This series is the input after repairing.
Note: AR method use AR(1) model. Input value should be auto-correlated, or the function would output a single point (0, 0.0).
When method is “linear” or the default, Screen method is used to impute.
Input series:
+-----------------------------+---------------+ | Time|root.test.d2.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| NaN| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| NaN| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| NaN| |2020-01-01T00:00:22.000+08:00| NaN| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| 128.0| +-----------------------------+---------------+
SQL for query:
select valuefill(s1) from root.test.d2
Output series:
+-----------------------------+-----------------------+ | Time|valuefill(root.test.d2)| +-----------------------------+-----------------------+ |2020-01-01T00:00:02.000+08:00| NaN| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 108.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.7| |2020-01-01T00:00:22.000+08:00| 121.3| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| 128.0| +-----------------------------+-----------------------+
When method is “previous”, previous method is used.
Input series is the same as above, the SQL for query is shown below:
select valuefill(s1,"method"="previous") from root.test.d2
Output series:
+-----------------------------+-------------------------------------------+ | Time|valuefill(root.test.d2,"method"="previous")| +-----------------------------+-------------------------------------------+ |2020-01-01T00:00:02.000+08:00| NaN| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 110.5| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 116.0| |2020-01-01T00:00:22.000+08:00| 116.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| 128.0| +-----------------------------+-------------------------------------------+
create function valuerepair as 'org.apache.iotdb.library.drepair.UDTFValueRepair'
This function is used to repair the value of the time series. Currently, two methods are supported: Screen is a method based on speed threshold, which makes all speeds meet the threshold requirements under the premise of minimum changes; LsGreedy is a method based on speed change likelihood, which models speed changes as Gaussian distribution, and uses a greedy algorithm to maximize the likelihood.
Name: VALUEREPAIR
Input Series: Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
method: The method used to repair, which is ‘Screen’ or ‘LsGreedy’. By default, Screen is used.minSpeed: This parameter is only valid with Screen. It is the speed threshold. Speeds below it will be regarded as outliers. By default, it is the median minus 3 times of median absolute deviation.maxSpeed: This parameter is only valid with Screen. It is the speed threshold. Speeds above it will be regarded as outliers. By default, it is the median plus 3 times of median absolute deviation.center: This parameter is only valid with LsGreedy. It is the center of the Gaussian distribution of speed changes. By default, it is 0.sigma: This parameter is only valid with LsGreedy. It is the standard deviation of the Gaussian distribution of speed changes. By default, it is the median absolute deviation.Output Series: Output a single series. The type is the same as the input. This series is the input after repairing.
Note: NaN will be filled with linear interpolation before repairing.
When method is ‘Screen’ or the default, Screen method is used.
Input series:
+-----------------------------+---------------+ | Time|root.test.d2.s1| +-----------------------------+---------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 126.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 100.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| NaN| +-----------------------------+---------------+
SQL for query:
select valuerepair(s1) from root.test.d2
Output series:
+-----------------------------+----------------------------+ | Time|valuerepair(root.test.d2.s1)| +-----------------------------+----------------------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 106.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| 128.0| +-----------------------------+----------------------------+
When method is ‘LsGreedy’, LsGreedy method is used.
Input series is the same as above, the SQL for query is shown below:
select valuerepair(s1,'method'='LsGreedy') from root.test.d2
Output series:
+-----------------------------+-------------------------------------------------+ | Time|valuerepair(root.test.d2.s1, "method"="LsGreedy")| +-----------------------------+-------------------------------------------------+ |2020-01-01T00:00:02.000+08:00| 100.0| |2020-01-01T00:00:03.000+08:00| 101.0| |2020-01-01T00:00:04.000+08:00| 102.0| |2020-01-01T00:00:06.000+08:00| 104.0| |2020-01-01T00:00:08.000+08:00| 106.0| |2020-01-01T00:00:10.000+08:00| 108.0| |2020-01-01T00:00:14.000+08:00| 112.0| |2020-01-01T00:00:15.000+08:00| 113.0| |2020-01-01T00:00:16.000+08:00| 114.0| |2020-01-01T00:00:18.000+08:00| 116.0| |2020-01-01T00:00:20.000+08:00| 118.0| |2020-01-01T00:00:22.000+08:00| 120.0| |2020-01-01T00:00:26.000+08:00| 124.0| |2020-01-01T00:00:28.000+08:00| 126.0| |2020-01-01T00:00:30.000+08:00| 128.0| +-----------------------------+-------------------------------------------------+
This function is used to clean time series with master data.
Name: MasterRepair Input Series: Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
omega: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.eta: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.k: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.output_column: The repaired column to output, defaults to 1 which means output the repair result of the first column.Output Series: Output a single series. The type is the same as the input. This series is the input after repairing.
Input series:
+-----------------------------+------------+------------+------------+------------+------------+------------+ | Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3| +-----------------------------+------------+------------+------------+------------+------------+------------+ |2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195| |2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193| |2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192| |2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194| |2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195| |2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193| |2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194| |2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190| +-----------------------------+------------+------------+------------+------------+------------+------------+
SQL for query:
select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
Output series:
+-----------------------------+-------------------------------------------------------------------------------------------+ | Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)| +-----------------------------+-------------------------------------------------------------------------------------------+ |2021-07-01T12:00:01.000+08:00| 1704| |2021-07-01T12:00:02.000+08:00| 1702| |2021-07-01T12:00:03.000+08:00| 1702| |2021-07-01T12:00:04.000+08:00| 1701| |2021-07-01T12:00:07.000+08:00| 1703| |2021-07-01T12:00:08.000+08:00| 1704| |2021-07-01T12:01:09.000+08:00| 1705| |2021-07-01T12:01:10.000+08:00| 1706| +-----------------------------+-------------------------------------------------------------------------------------------+
create function consecutivesequences as 'org.apache.iotdb.library.series.UDTFConsecutiveSequences'
This function is used to find locally longest consecutive subsequences in strictly equispaced multidimensional data.
Strictly equispaced data is the data whose time intervals are strictly equal. Missing data, including missing rows and missing values, is allowed in it, while data redundancy and timestamp drift is not allowed.
Consecutive subsequence is the subsequence that is strictly equispaced with the standard time interval without any missing data. If a consecutive subsequence is not a proper subsequence of any consecutive subsequence, it is locally longest.
Name: CONSECUTIVESEQUENCES
Input Series: Support multiple input series. The type is arbitrary but the data is strictly equispaced.
Parameters:
gap: The standard time interval which is a positive number with an unit. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. By default, it will be estimated by the mode of time intervals.Output Series: Output a single series. The type is INT32. Each data point in the output series corresponds to a locally longest consecutive subsequence. The output timestamp is the starting timestamp of the subsequence and the output value is the number of data points in the subsequence.
Note: For input series that is not strictly equispaced, there is no guarantee on the output.
It‘s able to manually specify the standard time interval by the parameter gap. It’s notable that false parameter leads to false output.
Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d1.s1|root.test.d1.s2| +-----------------------------+---------------+---------------+ |2020-01-01T00:00:00.000+08:00| 1.0| 1.0| |2020-01-01T00:05:00.000+08:00| 1.0| 1.0| |2020-01-01T00:10:00.000+08:00| 1.0| 1.0| |2020-01-01T00:20:00.000+08:00| 1.0| 1.0| |2020-01-01T00:25:00.000+08:00| 1.0| 1.0| |2020-01-01T00:30:00.000+08:00| 1.0| 1.0| |2020-01-01T00:35:00.000+08:00| 1.0| 1.0| |2020-01-01T00:40:00.000+08:00| 1.0| null| |2020-01-01T00:45:00.000+08:00| 1.0| 1.0| |2020-01-01T00:50:00.000+08:00| 1.0| 1.0| +-----------------------------+---------------+---------------+
SQL for query:
select consecutivesequences(s1,s2,'gap'='5m') from root.test.d1
Output series:
+-----------------------------+------------------------------------------------------------------+ | Time|consecutivesequences(root.test.d1.s1, root.test.d1.s2, "gap"="5m")| +-----------------------------+------------------------------------------------------------------+ |2020-01-01T00:00:00.000+08:00| 3| |2020-01-01T00:20:00.000+08:00| 4| |2020-01-01T00:45:00.000+08:00| 2| +-----------------------------+------------------------------------------------------------------+
When gap is default, this function estimates the standard time interval by the mode of time intervals and gets the same results. Therefore, this usage is more recommended.
Input series is the same as above, the SQL for query is shown below:
select consecutivesequences(s1,s2) from root.test.d1
Output series:
+-----------------------------+------------------------------------------------------+ | Time|consecutivesequences(root.test.d1.s1, root.test.d1.s2)| +-----------------------------+------------------------------------------------------+ |2020-01-01T00:00:00.000+08:00| 3| |2020-01-01T00:20:00.000+08:00| 4| |2020-01-01T00:45:00.000+08:00| 2| +-----------------------------+------------------------------------------------------+
create function consecutivewindows as 'org.apache.iotdb.library.series.UDTFConsecutiveWindows'
This function is used to find consecutive windows of specified length in strictly equispaced multidimensional data.
Strictly equispaced data is the data whose time intervals are strictly equal. Missing data, including missing rows and missing values, is allowed in it, while data redundancy and timestamp drift is not allowed.
Consecutive window is the subsequence that is strictly equispaced with the standard time interval without any missing data.
Name: CONSECUTIVEWINDOWS
Input Series: Support multiple input series. The type is arbitrary but the data is strictly equispaced.
Parameters:
gap: The standard time interval which is a positive number with an unit. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. By default, it will be estimated by the mode of time intervals.length: The length of the window which is a positive number with an unit. The unit is ‘ms’ for millisecond, ‘s’ for second, ‘m’ for minute, ‘h’ for hour and ‘d’ for day. This parameter cannot be lacked.Output Series: Output a single series. The type is INT32. Each data point in the output series corresponds to a consecutive window. The output timestamp is the starting timestamp of the window and the output value is the number of data points in the window.
Note: For input series that is not strictly equispaced, there is no guarantee on the output.
Input series:
+-----------------------------+---------------+---------------+ | Time|root.test.d1.s1|root.test.d1.s2| +-----------------------------+---------------+---------------+ |2020-01-01T00:00:00.000+08:00| 1.0| 1.0| |2020-01-01T00:05:00.000+08:00| 1.0| 1.0| |2020-01-01T00:10:00.000+08:00| 1.0| 1.0| |2020-01-01T00:20:00.000+08:00| 1.0| 1.0| |2020-01-01T00:25:00.000+08:00| 1.0| 1.0| |2020-01-01T00:30:00.000+08:00| 1.0| 1.0| |2020-01-01T00:35:00.000+08:00| 1.0| 1.0| |2020-01-01T00:40:00.000+08:00| 1.0| null| |2020-01-01T00:45:00.000+08:00| 1.0| 1.0| |2020-01-01T00:50:00.000+08:00| 1.0| 1.0| +-----------------------------+---------------+---------------+
SQL for query:
select consecutivewindows(s1,s2,'length'='10m') from root.test.d1
Output series:
+-----------------------------+--------------------------------------------------------------------+ | Time|consecutivewindows(root.test.d1.s1, root.test.d1.s2, "length"="10m")| +-----------------------------+--------------------------------------------------------------------+ |2020-01-01T00:00:00.000+08:00| 3| |2020-01-01T00:20:00.000+08:00| 3| |2020-01-01T00:25:00.000+08:00| 3| +-----------------------------+--------------------------------------------------------------------+
create function ar as 'org.apache.iotdb.library.dlearn.UDTFAR'
This function is used to learn the coefficients of the autoregressive models for a time series.
Name: AR
Input Series: Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
p: The order of the autoregressive model. Its default value is 1.Output Series: Output a single series. The type is DOUBLE. The first line corresponds to the first order coefficient, and so on.
Note:
p should be a positive integer.Input Series:
+-----------------------------+---------------+ | Time|root.test.d0.s0| +-----------------------------+---------------+ |2020-01-01T00:00:01.000+08:00| -4.0| |2020-01-01T00:00:02.000+08:00| -3.0| |2020-01-01T00:00:03.000+08:00| -2.0| |2020-01-01T00:00:04.000+08:00| -1.0| |2020-01-01T00:00:05.000+08:00| 0.0| |2020-01-01T00:00:06.000+08:00| 1.0| |2020-01-01T00:00:07.000+08:00| 2.0| |2020-01-01T00:00:08.000+08:00| 3.0| |2020-01-01T00:00:09.000+08:00| 4.0| +-----------------------------+---------------+
SQL for query:
select ar(s0,"p"="2") from root.test.d0
Output Series:
+-----------------------------+---------------------------+ | Time|ar(root.test.d0.s0,"p"="2")| +-----------------------------+---------------------------+ |1970-01-01T08:00:00.001+08:00| 0.9429| |1970-01-01T08:00:00.002+08:00| -0.2571| +-----------------------------+---------------------------+
This function is used to represent a time series.
Name: Representation
Input Series: Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
tb: The number of timestamp blocks. Its default value is 10.vb: The number of value blocks. Its default value is 10.Output Series: Output a single series. The type is INT32. The length is tb*vb. The timestamps starting from 0 only indicate the order.
Note:
tb and vb should be positive integers.Input Series:
+-----------------------------+---------------+ | Time|root.test.d0.s0| +-----------------------------+---------------+ |2020-01-01T00:00:01.000+08:00| -4.0| |2020-01-01T00:00:02.000+08:00| -3.0| |2020-01-01T00:00:03.000+08:00| -2.0| |2020-01-01T00:00:04.000+08:00| -1.0| |2020-01-01T00:00:05.000+08:00| 0.0| |2020-01-01T00:00:06.000+08:00| 1.0| |2020-01-01T00:00:07.000+08:00| 2.0| |2020-01-01T00:00:08.000+08:00| 3.0| |2020-01-01T00:00:09.000+08:00| 4.0| +-----------------------------+---------------+
SQL for query:
select representation(s0,"tb"="3","vb"="2") from root.test.d0
Output Series:
+-----------------------------+-------------------------------------------------+ | Time|representation(root.test.d0.s0,"tb"="3","vb"="2")| +-----------------------------+-------------------------------------------------+ |1970-01-01T08:00:00.001+08:00| 1| |1970-01-01T08:00:00.002+08:00| 1| |1970-01-01T08:00:00.003+08:00| 0| |1970-01-01T08:00:00.004+08:00| 0| |1970-01-01T08:00:00.005+08:00| 1| |1970-01-01T08:00:00.006+08:00| 1| +-----------------------------+-------------------------------------------------+
This function is used to calculate the matching score of two time series according to the representation.
Name: RM
Input Series: Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
Parameters:
tb: The number of timestamp blocks. Its default value is 10.vb: The number of value blocks. Its default value is 10.Output Series: Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
Note:
tb and vb should be positive integers.Input Series:
+-----------------------------+---------------+---------------+ | Time|root.test.d0.s0|root.test.d0.s1 +-----------------------------+---------------+---------------+ |2020-01-01T00:00:01.000+08:00| -4.0| -4.0| |2020-01-01T00:00:02.000+08:00| -3.0| -3.0| |2020-01-01T00:00:03.000+08:00| -3.0| -3.0| |2020-01-01T00:00:04.000+08:00| -1.0| -1.0| |2020-01-01T00:00:05.000+08:00| 0.0| 0.0| |2020-01-01T00:00:06.000+08:00| 1.0| 1.0| |2020-01-01T00:00:07.000+08:00| 2.0| 2.0| |2020-01-01T00:00:08.000+08:00| 3.0| 3.0| |2020-01-01T00:00:09.000+08:00| 4.0| 4.0| +-----------------------------+---------------+---------------+
SQL for query:
select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
Output Series:
+-----------------------------+-----------------------------------------------------+ | Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")| +-----------------------------+-----------------------------------------------------+ |1970-01-01T08:00:00.001+08:00| 1.00| +-----------------------------+-----------------------------------------------------+