com.cloudera.sparkts.models

RegressionARIMAModel

class RegressionARIMAModel extends TimeSeriesModel

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Instance Constructors

  1. new RegressionARIMAModel(regressionCoeff: Array[Double], arimaOrders: Array[Int], arimaCoeff: Array[Double])

    regressionCoeff

    coefficients for regression , including intercept , for example if model has 3 regressors then length of regressionCoeff is 4

    arimaOrders

    p,d,q for the arima error structure, length of arimaOrders must be 3

    arimaCoeff

    AR , d , and MA terms , length of arimaCoeff = p+d+q

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  6. def addTimeDependentEffects(ts: Vector, dest: Vector): Vector

    Takes a series of i.

    Takes a series of i.i.d. observations and returns a time series based on it with the time-dependent effects of this model added.

    ts

    Time series of i.i.d. observations.

    dest

    Array to put the filtered series, can be the same as ts.

    returns

    The dest series, for convenience.

    Definition Classes
    RegressionARIMAModelTimeSeriesModel
  7. val arimaCoeff: Array[Double]

    AR , d , and MA terms , length of arimaCoeff = p+d+q

  8. val arimaOrders: Array[Int]

    p,d,q for the arima error structure, length of arimaOrders must be 3

  9. final def asInstanceOf[T0]: T0

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  10. def clone(): AnyRef

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  11. final def eq(arg0: AnyRef): Boolean

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  13. def finalize(): Unit

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  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. val regressionCoeff: Array[Double]

    coefficients for regression , including intercept , for example if model has 3 regressors then length of regressionCoeff is 4

  21. def removeTimeDependentEffects(ts: Vector, dest: Vector): Vector

    Takes a time series that is assumed to have this model's characteristics and returns a time series with time-dependent effects of this model removed.

    Takes a time series that is assumed to have this model's characteristics and returns a time series with time-dependent effects of this model removed.

    This is the inverse of TimeSeriesModel#addTimeDependentEffects.

    ts

    Time series of observations with this model's characteristics.

    dest

    Array to put the filtered series, can be the same as ts.

    returns

    The dest series, for convenience.

    Definition Classes
    RegressionARIMAModelTimeSeriesModel
  22. final def synchronized[T0](arg0: ⇒ T0): T0

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  23. def toString(): String

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  24. final def wait(): Unit

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Inherited from TimeSeriesModel

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