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# Data Repairing
## TimestampRepair
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.
### Examples
#### Manually Specify the Standard Time Interval
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:
```sql
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|
+-----------------------------+----------------------------------------------------+
```
#### Automatically Estimate the Standard Time Interval
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:
```sql
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|
+-----------------------------+--------------------------------+
```
## ValueFill
### Usage
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).
### Examples
#### Fill with linear
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:
```sql
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|
+-----------------------------+-----------------------+
```
#### Previous Fill
When `method` is "previous", previous method is used.
Input series is the same as above, the SQL for query is shown below:
```sql
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|
+-----------------------------+-------------------------------------------+
```
## ValueRepair
### Usage
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.
### Examples
#### Repair with Screen
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:
```sql
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|
+-----------------------------+----------------------------+
```
#### Repair with LsGreedy
When `method` is 'LsGreedy', LsGreedy method is used.
Input series is the same as above, the SQL for query is shown below:
```sql
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|
+-----------------------------+-------------------------------------------------+
```
## MasterRepair
### Usage
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.
### Examples
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:
```sql
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|
+-----------------------------+-------------------------------------------------------------------------------------------+
```
## SeasonalRepair
### Usage
This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
**Name:** SEASONALREPAIR
**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
**Parameters:**
+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
+ `period`: It is the period of the time series.
+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
**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.
### Examples
#### Repair with Classical
When `method` is 'Classical' or default value, classical decomposition method is used.
Input series:
```
+-----------------------------+---------------+
| Time|root.test.d2.s1|
+-----------------------------+---------------+
|2020-01-01T00:00:02.000+08:00| 100.0|
|2020-01-01T00:00:04.000+08:00| 120.0|
|2020-01-01T00:00:06.000+08:00| 80.0|
|2020-01-01T00:00:08.000+08:00| 100.5|
|2020-01-01T00:00:10.000+08:00| 119.5|
|2020-01-01T00:00:12.000+08:00| 101.0|
|2020-01-01T00:00:14.000+08:00| 99.5|
|2020-01-01T00:00:16.000+08:00| 119.0|
|2020-01-01T00:00:18.000+08:00| 80.5|
|2020-01-01T00:00:20.000+08:00| 99.0|
|2020-01-01T00:00:22.000+08:00| 121.0|
|2020-01-01T00:00:24.000+08:00| 79.5|
+-----------------------------+---------------+
```
SQL for query:
```sql
select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
```
Output series:
```
+-----------------------------+--------------------------------------------------+
| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
+-----------------------------+--------------------------------------------------+
|2020-01-01T00:00:02.000+08:00| 100.0|
|2020-01-01T00:00:04.000+08:00| 120.0|
|2020-01-01T00:00:06.000+08:00| 80.0|
|2020-01-01T00:00:08.000+08:00| 100.5|
|2020-01-01T00:00:10.000+08:00| 119.5|
|2020-01-01T00:00:12.000+08:00| 87.0|
|2020-01-01T00:00:14.000+08:00| 99.5|
|2020-01-01T00:00:16.000+08:00| 119.0|
|2020-01-01T00:00:18.000+08:00| 80.5|
|2020-01-01T00:00:20.000+08:00| 99.0|
|2020-01-01T00:00:22.000+08:00| 121.0|
|2020-01-01T00:00:24.000+08:00| 79.5|
+-----------------------------+--------------------------------------------------+
```
#### Repair with Improved
When `method` is 'Improved', improved decomposition method is used.
Input series is the same as above, the SQL for query is shown below:
```sql
select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
```
Output series:
```
+-----------------------------+-------------------------------------------------------------+
| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
+-----------------------------+-------------------------------------------------------------+
|2020-01-01T00:00:02.000+08:00| 100.0|
|2020-01-01T00:00:04.000+08:00| 120.0|
|2020-01-01T00:00:06.000+08:00| 80.0|
|2020-01-01T00:00:08.000+08:00| 100.5|
|2020-01-01T00:00:10.000+08:00| 119.5|
|2020-01-01T00:00:12.000+08:00| 81.5|
|2020-01-01T00:00:14.000+08:00| 99.5|
|2020-01-01T00:00:16.000+08:00| 119.0|
|2020-01-01T00:00:18.000+08:00| 80.5|
|2020-01-01T00:00:20.000+08:00| 99.0|
|2020-01-01T00:00:22.000+08:00| 121.0|
|2020-01-01T00:00:24.000+08:00| 79.5|
+-----------------------------+-------------------------------------------------------------+
```