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object Regression4TS extends ModelFactory

The Regression4TS companion object provides factory functions and functions for creating functional forms.

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  4. def allForms(x: MatriD, lag1: Int, lag2: Int, addOne: Boolean = false): MatriD

    Create all forms/terms by concatenating columnwise the data matrix with lags 'lag1' to 'lag2', where 'lag1 = 0' is current values.

    Create all forms/terms by concatenating columnwise the data matrix with lags 'lag1' to 'lag2', where 'lag1 = 0' is current values. Note 'allForms (x) = allForms (x, 0, 2)'

    x

    the original un-expanded input/data matrix

    lag1

    the first lag included (inclusive)

    lag2

    the last lag included (inclusive)

    addOne

    whether to add a column of all ones to the matrix (intercept)

  5. def allForms(x: MatriD): MatriD

    Create all forms/terms by concatenating columnwise the data matrix with its first lag.

    Create all forms/terms by concatenating columnwise the data matrix with its first lag.

    x

    the original un-expanded input/data matrix

    Definition Classes
    Regression4TS → ModelFactory
  6. def allForms_xy(x: MatriD, y: VectoD, lag1: Int, lag2: Int, addOne: Boolean = false): MatriD

    Create all forms/terms by concatenating columnwise the data matrix with lags 'lag1' to 'lag2', where 'lag1 = 0' is current values.

    Create all forms/terms by concatenating columnwise the data matrix with lags 'lag1' to 'lag2', where 'lag1 = 0' is current values. Include lags for the response variable as well.

    x

    the original un-expanded input/data matrix

    y

    the original un-expanded output/response vector

    lag1

    the first lag included (inclusive)

    lag2

    the last lag included (inclusive)

    addOne

    whether to add a column of all ones to the matrix (intercept)

  7. def allForms_y(y: VectoD, lag1: Int, lag2: Int, addOne: Boolean = false): MatriD

    Create all forms/terms by concatenating columnwise the response vector lags 'lag1' to 'lag2', where 'lag1 = 0' is current values.

    Create all forms/terms by concatenating columnwise the response vector lags 'lag1' to 'lag2', where 'lag1 = 0' is current values.

    y

    the original un-expanded output/response vector

    lag1

    the first lag included (inclusive) required to be > 0

    lag2

    the last lag included (inclusive)

    addOne

    whether to add a column of all ones to the matrix (intercept)

  8. def allForms_y_dir(y: VectoD, lag1: Int, lag2: Int, h: Int, addOne: Boolean = false): MatriD

    Create all forms/terms by concatenating columnwise the response vector lags 'lag1' to 'lag2', where 'lag1 = 0' is current values.

    Create all forms/terms by concatenating columnwise the response vector lags 'lag1' to 'lag2', where 'lag1 = 0' is current values. This method is used for the direct forecasting method, where the model is explictly trained for an 'h'-step ahead forecast.

    y

    the original un-expanded output/response vector

    lag1

    the first lag included (inclusive) required to be > 0

    lag2

    the last lag included (inclusive)

    h

    the forecasting horizon for the model to be trained on.

    addOne

    whether to add a column of all ones to the matrix (intercept)

    See also

    arxiv.org/pdf/1108.3259.pdf

  9. def apply(x: MatriD, y: VectoD, fname: Strings, hparam: HyperParameter, technique: RegTechnique.RegTechnique, addOne: Boolean): Regression4TS

    Create a Regression4TS object from a data matrix and a response vector.

    Create a Regression4TS object from a data matrix and a response vector. This factory function provides data rescaling.

    x

    the initial data/input matrix (before quadratic term expansion)

    y

    the response/output m-vector

    fname

    the feature/variable names (use null for default)

    hparam

    the hyper-parameters (use null for default)

    technique

    the technique used to solve for b in x.t*x*b = x.t*y (use OR for default)

    addOne

    whether to add a column of all ones to the data matrix

    See also

    ModelFactory

  10. def apply(xy: MatriD, fname: Strings = null, hparam: HyperParameter = hp, technique: RegTechnique.RegTechnique = QR, addOne: Boolean = true): Regression4TS

    Create a Regression4TS object from a combined data-response matrix.

    Create a Regression4TS object from a combined data-response matrix.

    xy

    the initial combined data-response matrix (before quadratic term expansion)

    hparam

    the hyper-parameters

    technique

    the technique used to solve for b in x.t*x*b = x.t*y

    addOne

    whether to add a column of all ones to the data matrix

  11. final def asInstanceOf[T0]: T0
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  13. final def eq(arg0: AnyRef): Boolean
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  15. final def flaw(method: String, message: String): Unit
    Definition Classes
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  16. def forms(xi: VectoD, k: Int, nt: Int): VectoD

    Given a vector/point 'v', compute the values for all of its forms/terms, returning them as a vector (assumes Regression with intercept).

    Given a vector/point 'v', compute the values for all of its forms/terms, returning them as a vector (assumes Regression with intercept). Override for expanded columns, e.g., QuadRegression.

    xi

    the vector/point (i-th row of x) for creating forms/terms

    k

    the number of features/predictor variables (not counting intercept)

    nt

    the number of terms

    Definition Classes
    ModelFactory
  17. final def getClass(): Class[_]
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  18. def hashCode(): Int
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  19. val hp: HyperParameter

    Base hyper-parameter specification for Regression4TS

  20. final def isInstanceOf[T0]: Boolean
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  21. final def ne(arg0: AnyRef): Boolean
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  24. def numTerms(k: Int): Int

    The number of terms include current value and lag one value.

    The number of terms include current value and lag one value. when there are no cross-terms.

    k

    number of features/predictor variables (not counting intercept)

    Definition Classes
    Regression4TS → ModelFactory
  25. val rescale: Boolean

    The 'rescale' flag indicated whether the data is to be rescaled/normalized

    The 'rescale' flag indicated whether the data is to be rescaled/normalized

    Attributes
    protected
    Definition Classes
    ModelFactory
  26. def rescaleOff(): Unit

    Turn rescaling off.

    Turn rescaling off.

    Definition Classes
    ModelFactory
  27. def rescaleOn(): Unit

    Turn rescaling on.

    Turn rescaling on.

    Definition Classes
    ModelFactory
  28. final def synchronized[T0](arg0: ⇒ T0): T0
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