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trait Forecaster extends Error

The Forecaster trait provides a common framework for several forecasters. Note, the 'train' method must be called first followed by 'eval'.

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Abstract Value Members

  1. abstract def eval(): Unit

    Compute the error and useful diagnostics for the entire dataset.

  2. abstract def forecast(h: Int): VectoD

    Produce forecasts for 'h' steps ahead into the future

    Produce forecasts for 'h' steps ahead into the future

    h

    the forecasting horizon, number of steps ahead to produce forecasts

  3. abstract def predict(): VectoD

    Return the vector of predicted values on the training data

  4. abstract def train(): Forecaster

    Given a time series 'y', train the prediction function 'y = f(y_)', where 'f(y_)' is a function of the lagged values of 'y', by fitting its parameters.

Concrete Value Members

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

    Compute diagnostics for the forecaster.

    Compute diagnostics for the forecaster. Override to add more diagnostics. Note, for 'mse' and 'rmse', 'sse' is divided by the number of instances 'm' rather than the degrees of freedom.

    yy

    the response vector, actual values

    ee

    the residual/error vector

    Attributes
    protected
    See also

    en.wikipedia.org/wiki/Mean_squared_error

  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  8. def equals(arg0: Any): Boolean
    Definition Classes
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  9. def f_(z: Double): String

    Format a double value.

    Format a double value.

    z

    the double value to format

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

    Return the quality of fit including 'sst', 'sse', 'mae', rmse' and 'rSq'.

    Return the quality of fit including 'sst', 'sse', 'mae', rmse' and 'rSq'. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Note that 'rSq' is the last or number 5 measure. Override to add more quality of fit measures.

  12. def fitLabel: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

  13. def fitMap: Map[String, String]

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered).

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered). Override to add more quality of fit measures.

  14. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  15. def forecast(): VectoD

    Produce forecasts for one step ahead into the future

  16. final def getClass(): Class[_]
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    @native()
  17. def hashCode(): Int
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    @native()
  18. val index_rSq: Int
  19. final def isInstanceOf[T0]: Boolean
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  20. val mae: Double
    Attributes
    protected
  21. val mape: Double
    Attributes
    protected
  22. val mse: Double
    Attributes
    protected
  23. final def ne(arg0: AnyRef): Boolean
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  24. final def notify(): Unit
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    @native()
  25. final def notifyAll(): Unit
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    @native()
  26. val rSq: Double
    Attributes
    protected
  27. val rmse: Double
    Attributes
    protected
  28. val sse: Double
    Attributes
    protected
  29. val ssr: Double
    Attributes
    protected
  30. val sst: Double
    Attributes
    protected
  31. final def synchronized[T0](arg0: ⇒ T0): T0
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  32. def toString(): String
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  33. final def wait(): Unit
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    @throws( ... )
  34. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  35. final def wait(arg0: Long): Unit
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    @native() @throws( ... )

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