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# Data Profiling

## ACF

### Usage

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.

### Examples

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:

```sql
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|
+-----------------------------+--------------------+
```

## Distinct

### Usage

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:**

+ The timestamp of the output series is meaningless. The output order is arbitrary.
+ Missing points and null points in the input series will be ignored, but `NaN` will not.
+ Case Sensitive.


### Examples

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:

```sql
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|
+-----------------------------+-------------------------+
```

## Histogram

### Usage

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:**

+ If the value is lower than `min`, it will be put into the 1st bucket. If the value is larger than `max`, it will be put into the last bucket.
+ Missing points, null points and `NaN` in the input series will be ignored.

### Examples

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:

```sql
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|
+-----------------------------+---------------------------------------------------------------+
```

## Integral

### Usage

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.

### Examples

#### Default Parameters

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:

```sql
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:
$$\frac{1}{2}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] = 57.5$$

#### Specific time unit

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:

```sql
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:
$$\frac{1}{2\times 60}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] = 0.958$$

## IntegralAvg

### Usage

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.

### Examples

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:

```sql
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:
$$\frac{1}{2}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] / 10 = 5.75$$

## Mad

### Usage

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.

### Examples

#### Exact Query

With the default `error`(`error`=0), the function queries the exact MAD.

Input series:

```
+-----------------------------+------------+
|                         Time|root.test.s0|
+-----------------------------+------------+
|2021-03-17T10:32:17.054+08:00|   0.5319929|
|2021-03-17T10:32:18.054+08:00|   0.9304316|
|2021-03-17T10:32:19.054+08:00|  -1.4800133|
|2021-03-17T10:32:20.054+08:00|   0.6114087|
|2021-03-17T10:32:21.054+08:00|   2.5163336|
|2021-03-17T10:32:22.054+08:00|  -1.0845392|
|2021-03-17T10:32:23.054+08:00|   1.0562582|
|2021-03-17T10:32:24.054+08:00|   1.3867859|
|2021-03-17T10:32:25.054+08:00| -0.45429882|
|2021-03-17T10:32:26.054+08:00|   1.0353678|
|2021-03-17T10:32:27.054+08:00|   0.7307929|
|2021-03-17T10:32:28.054+08:00|   2.3167255|
|2021-03-17T10:32:29.054+08:00|    2.342443|
|2021-03-17T10:32:30.054+08:00|   1.5809103|
|2021-03-17T10:32:31.054+08:00|   1.4829416|
|2021-03-17T10:32:32.054+08:00|   1.5800357|
|2021-03-17T10:32:33.054+08:00|   0.7124368|
|2021-03-17T10:32:34.054+08:00| -0.78597564|
|2021-03-17T10:32:35.054+08:00|   1.2058644|
|2021-03-17T10:32:36.054+08:00|   1.4215064|
|2021-03-17T10:32:37.054+08:00|   1.2808295|
|2021-03-17T10:32:38.054+08:00|  -0.6173715|
|2021-03-17T10:32:39.054+08:00|  0.06644377|
|2021-03-17T10:32:40.054+08:00|    2.349338|
|2021-03-17T10:32:41.054+08:00|   1.7335888|
|2021-03-17T10:32:42.054+08:00|   1.5872132|
............
Total line number = 10000
```

SQL for query:

```sql
select mad(s0) from root.test
```

Output series:

```
+-----------------------------+------------------+
|                         Time| mad(root.test.s0)|
+-----------------------------+------------------+
|1970-01-01T08:00:00.000+08:00|0.6806197166442871|
+-----------------------------+------------------+
```

#### Approximate Query

By setting `error` within (0,1), the function queries the approximate MAD.

SQL for query:

```sql
select mad(s0, "error"="0.01") from root.test
```

Output series:

```
+-----------------------------+---------------------------------+
|                         Time|mad(root.test.s0, "error"="0.01")|
+-----------------------------+---------------------------------+
|1970-01-01T08:00:00.000+08:00|               0.6806616245859518|
+-----------------------------+---------------------------------+
```

## Median

### Usage

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.

### Examples

Input series:

```
+-----------------------------+------------+
|                         Time|root.test.s0|
+-----------------------------+------------+
|2021-03-17T10:32:17.054+08:00|   0.5319929|
|2021-03-17T10:32:18.054+08:00|   0.9304316|
|2021-03-17T10:32:19.054+08:00|  -1.4800133|
|2021-03-17T10:32:20.054+08:00|   0.6114087|
|2021-03-17T10:32:21.054+08:00|   2.5163336|
|2021-03-17T10:32:22.054+08:00|  -1.0845392|
|2021-03-17T10:32:23.054+08:00|   1.0562582|
|2021-03-17T10:32:24.054+08:00|   1.3867859|
|2021-03-17T10:32:25.054+08:00| -0.45429882|
|2021-03-17T10:32:26.054+08:00|   1.0353678|
|2021-03-17T10:32:27.054+08:00|   0.7307929|
|2021-03-17T10:32:28.054+08:00|   2.3167255|
|2021-03-17T10:32:29.054+08:00|    2.342443|
|2021-03-17T10:32:30.054+08:00|   1.5809103|
|2021-03-17T10:32:31.054+08:00|   1.4829416|
|2021-03-17T10:32:32.054+08:00|   1.5800357|
|2021-03-17T10:32:33.054+08:00|   0.7124368|
|2021-03-17T10:32:34.054+08:00| -0.78597564|
|2021-03-17T10:32:35.054+08:00|   1.2058644|
|2021-03-17T10:32:36.054+08:00|   1.4215064|
|2021-03-17T10:32:37.054+08:00|   1.2808295|
|2021-03-17T10:32:38.054+08:00|  -0.6173715|
|2021-03-17T10:32:39.054+08:00|  0.06644377|
|2021-03-17T10:32:40.054+08:00|    2.349338|
|2021-03-17T10:32:41.054+08:00|   1.7335888|
|2021-03-17T10:32:42.054+08:00|   1.5872132|
............
Total line number = 10000
```

SQL for query:

```sql
select median(s0, "error"="0.01") from root.test
```

Output series:

```
+-----------------------------+------------------------------------+
|                         Time|median(root.test.s0, "error"="0.01")|
+-----------------------------+------------------------------------+
|1970-01-01T08:00:00.000+08:00|                   1.021884560585022|
+-----------------------------+------------------------------------+
```

## MinMax

### Usage

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.

### Examples

#### Batch computing

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:

```sql
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|
+-----------------------------+--------------------+
```

## Mode

### Usage

This function is used to calculate the mode of time series, that is, the value that occurs most frequently.

**Name:** MODE

**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. There is only one data point in the series, whose timestamp is the same as which the first mode value has and value is the mode.

**Note:**

+ If there are multiple values with the most occurrences, the arbitrary one will be output.
+ Missing points and null points in the input series will be ignored, but `NaN` will not.

### Examples

Input series:

```
+-----------------------------+---------------+
|                         Time|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|          Hello|
|1970-01-01T08:00:00.004+08:00|          World|
|1970-01-01T08:00:00.005+08:00|          World|
|1970-01-01T08:00:01.600+08:00|          World|
|1970-01-15T09:37:34.451+08:00|          Hello|
|1970-01-15T09:37:34.452+08:00|          hello|
|1970-01-15T09:37:34.453+08:00|          Hello|
|1970-01-15T09:37:34.454+08:00|          World|
|1970-01-15T09:37:34.455+08:00|          World|
+-----------------------------+---------------+
```

SQL for query:

```sql
select mode(s2) from root.test.d2
```

Output series:

```
+-----------------------------+---------------------+
|                         Time|mode(root.test.d2.s2)|
+-----------------------------+---------------------+
|1970-01-01T08:00:00.004+08:00|                World|
+-----------------------------+---------------------+
```

## MvAvg

### Usage

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.

### Examples

#### Batch computing

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:

```sql
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|
+-----------------------------+---------------------------------+
```

## PACF

### Usage

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.

### Examples

#### Assigning maximum lag

Input series:

```
+-----------------------------+------------+
|                         Time|root.test.s1|
+-----------------------------+------------+
|2019-12-27T00:00:00.000+08:00|         5.0|
|2019-12-27T00:05:00.000+08:00|         5.0|
|2019-12-27T00:10:00.000+08:00|         5.0|
|2019-12-27T00:15:00.000+08:00|         5.0|
|2019-12-27T00:20:00.000+08:00|         6.0|
|2019-12-27T00:25:00.000+08:00|         5.0|
|2019-12-27T00:30:00.000+08:00|         6.0|
|2019-12-27T00:35:00.000+08:00|         6.0|
|2019-12-27T00:40:00.000+08:00|         6.0|
|2019-12-27T00:45:00.000+08:00|         6.0|
|2019-12-27T00:50:00.000+08:00|         6.0|
|2019-12-27T00:55:00.000+08:00|    5.982609|
|2019-12-27T01:00:00.000+08:00|   5.9652176|
|2019-12-27T01:05:00.000+08:00|    5.947826|
|2019-12-27T01:10:00.000+08:00|   5.9304347|
|2019-12-27T01:15:00.000+08:00|   5.9130435|
|2019-12-27T01:20:00.000+08:00|   5.8956523|
|2019-12-27T01:25:00.000+08:00|    5.878261|
|2019-12-27T01:30:00.000+08:00|   5.8608694|
|2019-12-27T01:35:00.000+08:00|    5.843478|
............
Total line number = 18066
```

SQL for query:

```sql
select pacf(s1, "lag"="5") from root.test
```

Output series:

```
+-----------------------------+-----------------------------+
|                         Time|pacf(root.test.s1, "lag"="5")|
+-----------------------------+-----------------------------+
|2019-12-27T00:00:00.000+08:00|                          1.0|
|2019-12-27T00:05:00.000+08:00|           0.3528915091942786|
|2019-12-27T00:10:00.000+08:00|           0.1761346122516304|
|2019-12-27T00:15:00.000+08:00|           0.1492391973294682|
|2019-12-27T00:20:00.000+08:00|          0.03560059645868398|
|2019-12-27T00:25:00.000+08:00|           0.0366222998995286|
+-----------------------------+-----------------------------+
```

## Percentile

### Usage

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.

### Examples

Input series:

```
+-----------------------------+------------+
|                         Time|root.test.s0|
+-----------------------------+------------+
|2021-03-17T10:32:17.054+08:00|   0.5319929|
|2021-03-17T10:32:18.054+08:00|   0.9304316|
|2021-03-17T10:32:19.054+08:00|  -1.4800133|
|2021-03-17T10:32:20.054+08:00|   0.6114087|
|2021-03-17T10:32:21.054+08:00|   2.5163336|
|2021-03-17T10:32:22.054+08:00|  -1.0845392|
|2021-03-17T10:32:23.054+08:00|   1.0562582|
|2021-03-17T10:32:24.054+08:00|   1.3867859|
|2021-03-17T10:32:25.054+08:00| -0.45429882|
|2021-03-17T10:32:26.054+08:00|   1.0353678|
|2021-03-17T10:32:27.054+08:00|   0.7307929|
|2021-03-17T10:32:28.054+08:00|   2.3167255|
|2021-03-17T10:32:29.054+08:00|    2.342443|
|2021-03-17T10:32:30.054+08:00|   1.5809103|
|2021-03-17T10:32:31.054+08:00|   1.4829416|
|2021-03-17T10:32:32.054+08:00|   1.5800357|
|2021-03-17T10:32:33.054+08:00|   0.7124368|
|2021-03-17T10:32:34.054+08:00| -0.78597564|
|2021-03-17T10:32:35.054+08:00|   1.2058644|
|2021-03-17T10:32:36.054+08:00|   1.4215064|
|2021-03-17T10:32:37.054+08:00|   1.2808295|
|2021-03-17T10:32:38.054+08:00|  -0.6173715|
|2021-03-17T10:32:39.054+08:00|  0.06644377|
|2021-03-17T10:32:40.054+08:00|    2.349338|
|2021-03-17T10:32:41.054+08:00|   1.7335888|
|2021-03-17T10:32:42.054+08:00|   1.5872132|
............
Total line number = 10000
```

SQL for query:

```sql
select percentile(s0, "rank"="0.2", "error"="0.01") from root.test
```

Output series:

```
+-----------------------------+------------------------------------------------------+
|                         Time|percentile(root.test.s0, "rank"="0.2", "error"="0.01")|
+-----------------------------+------------------------------------------------------+
|2021-03-17T10:35:02.054+08:00|                                    0.1801469624042511|
+-----------------------------+------------------------------------------------------+
```

## Quantile

### Usage

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.

### Examples

Input series:

```
+-----------------------------+------------+
|                         Time|root.test.s0|
+-----------------------------+------------+
|2021-03-17T10:32:17.054+08:00|   0.5319929|
|2021-03-17T10:32:18.054+08:00|   0.9304316|
|2021-03-17T10:32:19.054+08:00|  -1.4800133|
|2021-03-17T10:32:20.054+08:00|   0.6114087|
|2021-03-17T10:32:21.054+08:00|   2.5163336|
|2021-03-17T10:32:22.054+08:00|  -1.0845392|
|2021-03-17T10:32:23.054+08:00|   1.0562582|
|2021-03-17T10:32:24.054+08:00|   1.3867859|
|2021-03-17T10:32:25.054+08:00| -0.45429882|
|2021-03-17T10:32:26.054+08:00|   1.0353678|
|2021-03-17T10:32:27.054+08:00|   0.7307929|
|2021-03-17T10:32:28.054+08:00|   2.3167255|
|2021-03-17T10:32:29.054+08:00|    2.342443|
|2021-03-17T10:32:30.054+08:00|   1.5809103|
|2021-03-17T10:32:31.054+08:00|   1.4829416|
|2021-03-17T10:32:32.054+08:00|   1.5800357|
|2021-03-17T10:32:33.054+08:00|   0.7124368|
|2021-03-17T10:32:34.054+08:00| -0.78597564|
|2021-03-17T10:32:35.054+08:00|   1.2058644|
|2021-03-17T10:32:36.054+08:00|   1.4215064|
|2021-03-17T10:32:37.054+08:00|   1.2808295|
|2021-03-17T10:32:38.054+08:00|  -0.6173715|
|2021-03-17T10:32:39.054+08:00|  0.06644377|
|2021-03-17T10:32:40.054+08:00|    2.349338|
|2021-03-17T10:32:41.054+08:00|   1.7335888|
|2021-03-17T10:32:42.054+08:00|   1.5872132|
............
Total line number = 10000
```

SQL for query:

```sql
select quantile(s0, "rank"="0.2", "K"="800") from root.test
```

Output series:

```
+-----------------------------+------------------------------------------------------+
|                         Time|quantile(root.test.s0, "rank"="0.2", "K"="800")|
+-----------------------------+------------------------------------------------------+
|1970-01-01T08:00:00.000+08:00|                                    0.1801469624042511|
+-----------------------------+------------------------------------------------------+
```

## Period

### Usage

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.

### Examples

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:

```sql
select period(s1) from root.test.d3
```

Output series:

```
+-----------------------------+-----------------------+
|                         Time|period(root.test.d3.s1)|
+-----------------------------+-----------------------+
|1970-01-01T08:00:00.000+08:00|                      3|
+-----------------------------+-----------------------+
```

## QLB

### Usage

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.

### Examples

#### Using Default Parameter

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:

```sql
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|
+-----------------------------+--------------------+
```

## Resample

### Usage

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.

### Examples

#### Up-sampling

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:

```sql
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|
+-----------------------------+----------------------------------------------------------+
```

#### Down-sampling

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:

```sql
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|
+-----------------------------+--------------------------------------------------------+
```


#### Specify the time period

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:

```sql
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|
+-----------------------------+-----------------------------------------------------------------------+
```

## Sample

### Usage

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](http://skemman.is/stream/get/1946/15343/37285/3/SS_MSthesis.pdf)

**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.

### Examples

#### Reservoir Sampling

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:

```sql
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|
+-----------------------------+------------------------------------------------------+
```

#### Isometric Sampling

When `method` is 'isometric', isometric sampling is used.

Input series is the same as above, the SQL for query is shown below:

```sql
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|
+-----------------------------+------------------------------------------------------+
```

## Segment

### Usage

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.

### Examples

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:

```sql
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|
+-----------------------------+------------------------------------+
```

## Skew

### Usage

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.

### Examples

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:

```sql
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|
+-----------------------------+-----------------------+
```

## Spline

### Usage

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.

### Examples

#### Assigning number of interpolation points

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:

```sql
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|
+-----------------------------+------------------------------------+
```

## Spread

### Usage

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.

### Examples

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:

```sql
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|
+-----------------------------+-----------------------+
```

## Stddev

### Usage

This function is used to calculate the population standard deviation.

**Name:** STDDEV

**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 standard deviation.

**Note:** Missing points, null points and `NaN` in the input series will be ignored.

### Examples

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:

```sql
select stddev(s1) from root.test.d1
```

Output series:

```
+-----------------------------+-----------------------+
|                         Time|stddev(root.test.d1.s1)|
+-----------------------------+-----------------------+
|1970-01-01T08:00:00.000+08:00|     5.7662812973353965|
+-----------------------------+-----------------------+
```

## ZScore

### Usage

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.

### Examples

#### Batch computing

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:

```sql
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|
+-----------------------------+--------------------+
```