timeseries

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Version 4.1.2  by WSO2

Category : Siddhi Execution
Supported Product Version : SP 4.0.0 SP 4.1.0 SP 4.2.0 SP 4.3.0 SP 4.4.0  

Summary

The siddhi-execution-timeseries extension is an extension to Siddhi which enables users to forecast and detect outliers in time series data, using Linear Regression Models.



Features provided by timeseries extension


  • kernelMinMax (Kernel based minima maxima detection)

    The kernalMinMax function uses Gaussian Kernel to smooth the time series values in the given window size, and then determine the maxima and minima of that set of values.
  • Forecast

    Siddhi allows you to forecast future events using linear regression on real time data streams.
    The forecast function uses a dependent event stream (Y), an independent event stream (X) and a user-specified next X value, and returns the forecast Y value based on the regression equation of the historical data.

    The two implementations of the forecast function can be distinguished as follows.

    • forecast: This allows you to specify a batch size (optional) that defines the number of events to be considered for the regression calculation when forecasting the Y value.
    • lengthTimeForecast: This allows you to restrict the number of events considered for the regression calculation when forecasting the Y value based on a specified time window and/or batch size.
  • Outlier

    Siddhi allows you to identify outliers using linear regression on real time data streams.
    The outlier function takes in a dependent event stream (Y), an independent event stream (X) and a user specified range for outliers, and returns an output to indicate whether the current event is an outlier based on the regression equation that fits historical data.

    The two implementations of outlier function can be distinguished as follows.

    • outlier: This allows you to specify a batch size (optional) that defines the number of events to be considered for the calculation of regression when finding outliers.
    • lengthTimeOutlier: This allows you to restrict the number of events considered for the regression calculation performed when finding outliers based on a specified time window and/or a batch size.
  • Regression

    The two implementations of regression could be distinguished as follows
    • regress: This allows you to specify the batch size (optional) that defines the number of events to be considered for the calculation of regression.
    • lengthTimeRegress: This allows you to specify the time window and batch size (required). The number of events considered for the regression calculation can be restricted based on the time window and/or the batch size.
  • kalmanMinMax (kalman based minima maxima detection)

    The kalmanMinMax function uses the kalman filter to smooth the time series values in the given window size, and then determine the maxima and minima of that set of values.

Extension


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