Packages

c

scalation.analytics

ExpSmoothing

class ExpSmoothing extends Predictor 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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Error, Predictor, AnyRef, Any
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  1. ExpSmoothing
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Visibility
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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. val b: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  6. def build(x: MatriD, y: VectoD): Predictor
    Definition Classes
    Predictor
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  9. def diagnose(yy: VectoD): Unit

    Compute diagostics for the Exponential Smoothing model.

    Compute diagostics for the Exponential Smoothing model.

    yy

    the response vector

    Attributes
    protected
    Definition Classes
    ExpSmoothingPredictor
  10. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def eval(yy: VectoD = y): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics.

    yy

    the response vector

    Definition Classes
    ExpSmoothingPredictor
  14. 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

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

    Return the quality of fit including 'rSquared'.

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

    Definition Classes
    ExpSmoothingPredictor
  17. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    ExpSmoothingPredictor
  18. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  19. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  20. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  21. val index_rSq: Int
    Definition Classes
    Predictor
  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  24. 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
    Predictor
  25. val mse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  26. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  28. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  29. 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
    ExpSmoothingPredictor
  30. 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
    Predictor
  31. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  32. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  33. val rmse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  34. 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

  35. val sse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  36. val ssr: Double
    Attributes
    protected
    Definition Classes
    Predictor
  37. val sst: Double
    Attributes
    protected
    Definition Classes
    Predictor
  38. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  39. def toString(): String
    Definition Classes
    AnyRef → Any
  40. 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
    ExpSmoothingPredictor
  41. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  42. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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