com.cloudera.sparkts.models

ARGARCHModel

class ARGARCHModel extends TimeSeriesModel

A GARCH(1, 1) + AR(1) model, where y(i) = c + phi * y(i - 1) + eta(i), and h(i), the variance of eta(i), is given by h(i) = omega + alpha * eta(i) ** 2 + beta * h(i - 1) ** 2

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TimeSeriesModel, AnyRef, Any
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Instance Constructors

  1. new ARGARCHModel(c: Double, phi: Double, omega: Double, alpha: Double, beta: Double)

    c

    The constant term.

    phi

    The autoregressive term.

    omega

    The constant term in the variance.

Value Members

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

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): 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
    ARGARCHModelTimeSeriesModel
  7. val alpha: Double

  8. final def asInstanceOf[T0]: T0

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  9. val beta: Double

  10. val c: Double

    The constant term.

  11. def clone(): AnyRef

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

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  13. def equals(arg0: Any): Boolean

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  14. def finalize(): Unit

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

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  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. val omega: Double

    The constant term in the variance.

  22. val phi: Double

    The autoregressive term.

  23. 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
    ARGARCHModelTimeSeriesModel
  24. def sample(n: Int, rand: RandomGenerator): Array[Double]

    Samples a random time series of a given length with the properties of the model.

    Samples a random time series of a given length with the properties of the model.

    n

    The length of the time series to sample.

    rand

    The random generator used to generate the observations.

    returns

    The samples time series.

  25. final def synchronized[T0](arg0: ⇒ T0): T0

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  26. def toString(): String

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  27. final def wait(): Unit

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  28. final def wait(arg0: Long, arg1: Int): Unit

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  29. final def wait(arg0: Long): Unit

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Inherited from TimeSeriesModel

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