Package org.elasticsearch.search.aggregations.pipeline

Class MovingFunctions

java.lang.Object
org.elasticsearch.search.aggregations.pipeline.MovingFunctions
public class MovingFunctions extends Object
Provides a collection of static utility methods that can be referenced from MovingFunction script contexts

  • Constructor Summary

    Constructors
    Constructor
    Description
    MovingFunctions()
     

  • Method Summary

    Modifier and Type
    Method
    Description
    static double
    ewma(double[] values, double alpha)
    Calculate a exponentially weighted moving average.
    static double
    holt(double[] values, double alpha, double beta)
    Calculate a doubly exponential weighted moving average Alpha controls the smoothing of the data.
    static double[]
    holtForecast(double[] values, double alpha, double beta, int numForecasts)
    Version of holt that can "forecast", not exposed as a whitelisted function for moving_fn scripts, but here as compatibility/code sharing for existing moving_avg agg.
    static double
    holtWinters(double[] values, double alpha, double beta, double gamma, int period, boolean multiplicative)
    Calculate a triple exponential weighted moving average Alpha controls the smoothing of the data.
    static double[]
    holtWintersForecast(double[] values, double alpha, double beta, double gamma, int period, double padding, boolean multiplicative, int numForecasts)
    Version of holt-winters that can "forecast", not exposed as a whitelisted function for moving_fn scripts, but here as compatibility/code sharing for existing moving_avg agg.
    static double
    linearWeightedAvg(double[] values)
    Calculate a linearly weighted moving average, such that older values are linearly less important.
    static double
    max(double[] values)
    Find the maximum value in a window of values.
    static double
    min(double[] values)
    Find the minimum value in a window of values If all values are missing/null/NaN, the return value will be NaN
    static double
    stdDev(double[] values, double avg)
    Calculate a standard deviation over the values using the provided average.
    static double
    sum(double[] values)
    Find the sum of a window of values If all values are missing/null/NaN, the return value will be 0.0
    static double
    unweightedAvg(double[] values)
    Calculate a simple unweighted (arithmetic) moving average.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

    • MovingFunctions

      public MovingFunctions()

  • Method Details

    • max

      public static double max(double[] values)
      Find the maximum value in a window of values. If all values are missing/null/NaN, the return value will be NaN

    • min

      public static double min(double[] values)
      Find the minimum value in a window of values If all values are missing/null/NaN, the return value will be NaN

    • sum

      public static double sum(double[] values)
      Find the sum of a window of values If all values are missing/null/NaN, the return value will be 0.0

    • unweightedAvg

      public static double unweightedAvg(double[] values)
      Calculate a simple unweighted (arithmetic) moving average. Only finite values are averaged. NaN or null are ignored. If all values are missing/null/NaN, the return value will be NaN. The average is based on the count of non-null, non-NaN values.

    • stdDev

      public static double stdDev(double[] values, double avg)
      Calculate a standard deviation over the values using the provided average. Only finite values are averaged. NaN or null are ignored. If all values are missing/null/NaN, the return value will be NaN. The average is based on the count of non-null, non-NaN values.

    • linearWeightedAvg

      public static double linearWeightedAvg(double[] values)
      Calculate a linearly weighted moving average, such that older values are linearly less important. "Time" is determined by position in collection Only finite values are averaged. NaN or null are ignored. If all values are missing/null/NaN, the return value will be NaN The average is based on the count of non-null, non-NaN values.

    • ewma

      public static double ewma(double[] values, double alpha)
      Calculate a exponentially weighted moving average. Alpha controls the smoothing of the data. Alpha = 1 retains no memory of past values (e.g. a random walk), while alpha = 0 retains infinite memory of past values (e.g. the series mean). Useful values are somewhere in between. Defaults to 0.5. Only finite values are averaged. NaN or null are ignored. If all values are missing/null/NaN, the return value will be NaN The average is based on the count of non-null, non-NaN values.
      Parameters:
      alpha - A double between 0-1 inclusive, controls data smoothing

    • holt

      public static double holt(double[] values, double alpha, double beta)
      Calculate a doubly exponential weighted moving average Alpha controls the smoothing of the data. Alpha = 1 retains no memory of past values (e.g. a random walk), while alpha = 0 retains infinite memory of past values (e.g. the series mean). Useful values are somewhere in between. Defaults to 0.5. Beta is equivalent to alpha, but controls the smoothing of the trend instead of the data Only finite values are averaged. NaN or null are ignored. If all values are missing/null/NaN, the return value will be NaN The average is based on the count of non-null, non-NaN values.
      Parameters:
      alpha - A double between 0-1 inclusive, controls data smoothing
      beta - a double between 0-1 inclusive, controls trend smoothing

    • holtForecast

      public static double[] holtForecast(double[] values, double alpha, double beta, int numForecasts)
      Version of holt that can "forecast", not exposed as a whitelisted function for moving_fn scripts, but here as compatibility/code sharing for existing moving_avg agg. Can be removed when moving_avg is gone.

    • holtWinters

      public static double holtWinters(double[] values, double alpha, double beta, double gamma, int period, boolean multiplicative)
      Calculate a triple exponential weighted moving average Alpha controls the smoothing of the data. Alpha = 1 retains no memory of past values (e.g. a random walk), while alpha = 0 retains infinite memory of past values (e.g. the series mean). Useful values are somewhere in between. Defaults to 0.5. Beta is equivalent to alpha, but controls the smoothing of the trend instead of the data. Gamma is equivalent to alpha, but controls the smoothing of the seasonality instead of the data Only finite values are averaged. NaN or null are ignored. If all values are missing/null/NaN, the return value will be NaN The average is based on the count of non-null, non-NaN values.
      Parameters:
      alpha - A double between 0-1 inclusive, controls data smoothing
      beta - a double between 0-1 inclusive, controls trend smoothing
      gamma - a double between 0-1 inclusive, controls seasonality smoothing
      period - the expected periodicity of the data
      multiplicative - true if multiplicative HW should be used. False for additive

    • holtWintersForecast

      public static double[] holtWintersForecast(double[] values, double alpha, double beta, double gamma, int period, double padding, boolean multiplicative, int numForecasts)
      Version of holt-winters that can "forecast", not exposed as a whitelisted function for moving_fn scripts, but here as compatibility/code sharing for existing moving_avg agg. Can be removed when moving_avg is gone.