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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 whenintervalis 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:
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:
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:
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:
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:
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:
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:
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:
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:
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|+-----------------------------+-------------------------------------------------------------+
