Packages

class ExpSmoothing extends Forecaster with Error

The ExpSmoothing class provide very basic time series analysis capabilities of Exponential Smoothing models. ExpSmoothing models are often used for forecasting. Given time series data stored in vector 'y', its next value 'y_t = y(t)' may be predicted based on prior/smoothed values of 'y':

y_t = s_t-1 + α (s_t-1 - s_t-2)

where vector 's' is the smoothed version of vector 'y' and 'α in [0, 1]' is the smoothing parameter. ------------------------------------------------------------------------------

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Instance Constructors

  1. new ExpSmoothing(y: VectoD, t: VectoD)

    y

    the input vector (time series data)

    t

    the time vector

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  6. def diagnose(yy: VectoD, ee: VectoD): Unit

    Compute diagostics for the Exponential Smoothing model.

    Compute diagostics for the Exponential Smoothing model.

    yy

    the response vector

    ee

    the error/residual vector

    Attributes
    protected
    Definition Classes
    ExpSmoothing → Forecaster
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def eval(yy: VectoD = y): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics.

    yy

    the response vector

    Definition Classes
    ExpSmoothing → Forecaster
  10. def eval(xx: MatriD, yy: VectoD): Unit

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    xx

    the test data matrix

    yy

    the test response vector FIX - implement in classes

    Definition Classes
    Forecaster
  11. def f_obj(αα: Double): Double

    The objective function to be minimized (sum of squared errors for the given 'αα').

    The objective function to be minimized (sum of squared errors for the given 'αα').

    αα

    the parameter of the objective function to be optimized

  12. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. def fit: VectoD

    Return the quality of fit including 'rSquared'.

    Return the quality of fit including 'rSquared'. Not providing 'rBarSq'.

    Definition Classes
    ExpSmoothing → Forecaster
  14. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    ExpSmoothing → Forecaster
  15. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. val index_rSq: Int
    Definition Classes
    Forecaster
  19. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  20. val mae: Double
    Attributes
    protected
    Definition Classes
    Forecaster
  21. def metrics: Map[String, Any]

    Build a map of selected quality of fit measures/metrics.

    Build a map of selected quality of fit measures/metrics.

    Definition Classes
    Forecaster
  22. val mse: Double
    Attributes
    protected
    Definition Classes
    Forecaster
  23. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  25. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  26. def predict(s: VectoD): Double

    Predict the next (unknown) value in the time-series.

    Predict the next (unknown) value in the time-series.

    s

    the smoothed time-series data

    Definition Classes
    ExpSmoothing → Forecaster
  27. def predict(z: VectoI): Double

    Given a new discrete data vector z, predict the y-value of f(z).

    Given a new discrete data vector z, predict the y-value of f(z).

    z

    the vector to use for prediction

    Definition Classes
    Forecaster
  28. val rSq: Double
    Attributes
    protected
    Definition Classes
    Forecaster
  29. val rmse: Double
    Attributes
    protected
    Definition Classes
    Forecaster
  30. def smooth(α_new: Double = α): VectoD

    Smooth the times series data.

    Smooth the times series data.

    α_new

    the new smoothing parameter, skip to use default

  31. val sse: Double
    Attributes
    protected
    Definition Classes
    Forecaster
  32. val ssr: Double
    Attributes
    protected
    Definition Classes
    Forecaster
  33. val sst: Double
    Attributes
    protected
    Definition Classes
    Forecaster
  34. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  35. def toString(): String
    Definition Classes
    AnyRef → Any
  36. def train(yy: VectoD = y): ExpSmoothing

    Train the ExpSmoothing model to times series data, by finding the value for the smoothing parameter 'α' that minimizes the sum of squared errors (sse).

    Train the ExpSmoothing model to times series data, by finding the value for the smoothing parameter 'α' that minimizes the sum of squared errors (sse). FIX - use either a penalty or cross-validation, else α -> 1

    yy

    the response vector

    Definition Classes
    ExpSmoothing → Forecaster
  37. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  38. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Error

Inherited from Forecaster

Inherited from AnyRef

Inherited from Any

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