com.cloudera.sparkts.models

EWMA

object EWMA

Fits an Exponentially Weight Moving Average model (EWMA) (aka. Simple Exponential Smoothing) to a time series. The model is defined as S_t = (1 - a) * X_t + a * S_{t - 1}, where a is the smoothing parameter, X is the original series, and S is the smoothed series. For more information, please see https://en.wikipedia.org/wiki/Exponential_smoothing.

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. EWMA
  2. AnyRef
  3. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Value Members

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

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

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. def fitModel(ts: Vector): EWMAModel

    Fits an EWMA model to a time series.

    Fits an EWMA model to a time series. Uses the first point in the time series as a starting value. Uses sum squared error as an objective function to optimize to find smoothing parameter The model for EWMA is recursively defined as S_t = (1 - a) * X_t + a * S_{t-1}, where a is the smoothing parameter, X is the original series, and S is the smoothed series Note that the optimization is performed as unbounded optimization, although in its formal definition the smoothing parameter is <= 1, which corresponds to an inequality bounded optimization. Given this, the resulting smoothing parameter should always be sanity checked https://en.wikipedia.org/wiki/Exponential_smoothing

    ts

    the time series to which we want to fit an EWMA model

    returns

    EWMA model

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

    Definition Classes
    AnyRef → Any
  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  15. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  16. final def notify(): Unit

    Definition Classes
    AnyRef
  17. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  18. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  19. def toString(): String

    Definition Classes
    AnyRef → Any
  20. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  21. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped