scalation
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scalation/scalation/scalation.modeling/scalation.modeling.forecasting/ARX

ARX

scalation.modeling.forecasting.ARX
See theARX companion class
object ARX extends MakeMatrix4TS

The ARX companion object provides factory methods for the ARX class.

Attributes

Companion
class
Graph
Supertypes
class Object
trait Matchable
class Any
Self type
ARX.type

Members list

Value members

Concrete methods

def apply(xe: MatrixD, y: VectorD, hh: Int, fname_: Array[String] = ..., tRng: Range = ..., hparam: HyperParameter = ..., fEndo_enab: LinkedHashSet[TransformT] = ..., fExo_enab: Array[LinkedHashSet[TransformT]] = ..., bakcast: Boolean = ...): ARX

Create an ARX object by building an input matrix xy and then calling the ARX constructor.

Create an ARX object by building an input matrix xy and then calling the ARX constructor.

Value parameters

bakcast

whether a backcasted value is prepended to the time series (defaults to false)

fEndo_enab

the set of transforms to be used for the endogenous

fExo_enab

the array containing the sets of transforms to be used for the exogenous

fname_

the feature/variable names

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters

tRng

the time range, if relevant (time index may suffice)

xe

the matrix of exogenous variable values

y

the endogenous/response vector (main time series data)

Attributes

def buildMatrix(xe_bfill: MatrixD, y: VectorD, hp_: HyperParameter, bakcast: Boolean): MatrixD

Build the input matrix by combining the spec + p columns for the trend and endogenous variable with the q * xe.dim2 columns for the exogenous variables. When cross = true, additional cross terms will be added. Columns produced by transformations will be added as well.

Build the input matrix by combining the spec + p columns for the trend and endogenous variable with the q * xe.dim2 columns for the exogenous variables. When cross = true, additional cross terms will be added. Columns produced by transformations will be added as well.

Value parameters

bakcast

whether a backcasted value is prepended to the time series (defaults to false)

hp_

the hyper-parameters

xe_bfill

the matrix of exogenous variable values

y

the endogenous/response vector (main time series data)

Attributes

def formNames(n_exo: Int, hp_: HyperParameter, n_fEn: Int = ..., n_fExArr: Array[Int] = ...): Array[String]

Form an array of names for the features included in the model.

Form an array of names for the features included in the model.

Value parameters

hp_

the hyper-parameters

n_exo

the number of exogenous variable

n_fEn

the number of functions used to map endogenous variables (none for ARX)

n_fExArr

the number of functions used to map exogenous variables (none for ARX)

Attributes

def rescale(xe: MatrixD, y: VectorD, hh: Int, fname_: Array[String] = ..., tRng: Range = ..., hparam: HyperParameter = ..., fEndo_enab: LinkedHashSet[TransformT] = ..., fExo_enab: Array[LinkedHashSet[TransformT]] = ..., bakcast: Boolean = ..., tFormT: TransformT = ...): ARX

Create an ARX object by building an input matrix xy and then calling the ARX constructor, with rescaling of endogneous and exogenous variable values.

Create an ARX object by building an input matrix xy and then calling the ARX constructor, with rescaling of endogneous and exogenous variable values.

Value parameters

bakcast

whether a backcasted value is prepended to the time series (defaults to false)

fEndo_enab

the set of transforms to be used for the endogenous

fExo_enab

the array containing the sets of transforms to be used for the exogenous

fname_

the feature/variable names

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters

tFormT

the transform for rescaling endogenous and exogenous

tRng

the time range, if relevant (time index may suffice)

xe

the matrix of exogenous variable values

y

the endogenous/response vector (main time series data)

Attributes

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