ARX_Symb

scalation.modeling.forecasting.ARX_Symb
See theARX_Symb companion class
object ARX_Symb extends MakeMatrix4TS

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

Attributes

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

Members list

Value members

Concrete methods

def apply(xe: MatrixD, y: VectorD, hh: Int, fname_: Array[String], tRng: Range, hparam: HyperParameter, fEndo: Array[Transform], fExo: Array[Transform], bakcast: Boolean): ARX_Symb

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

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

Value parameters

bakcast

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

fEndo

the array of functions used to transform endogenous variables

fExo

the array of functions used to transform exogenous variables

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: MatrixD, y: VectorD, hp_: HyperParameter, fEndo: Array[Transform], fExo: Array[Transform], bakcast: Boolean, tForm: (VectorD | MatrixD) => Transform): (MatrixD, TransformMap)

Build the input matrix by combining the p + spec columns for the trend and endogenous variable with the q * xe.dim2 columns for the exogenous variables.

Build the input matrix by combining the p + spec columns for the trend and endogenous variable with the q * xe.dim2 columns for the exogenous variables.

Value parameters

bakcast

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

hp_

the hyper-parameters

xe

the matrix of exogenous variable values

y_ypp

the response vector (time series data) and raised to power pp

Attributes

def formNames(n_exo: Int, hp_: HyperParameter, n_fEn: Int, n_fEx: 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

n_fEx

the number of functions used to map exogenous variables

Attributes

def rescale(xe: MatrixD, y: VectorD, hh: Int, fname_: Array[String], tRng: Range, hparam: HyperParameter, fEndo: Array[Transform], fExo: Array[Transform], bakcast: Boolean, tForm: (VectorD | MatrixD) => Transform): ARX_Symb

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

Create an ARX_Symb object by building an input matrix xy and then calling the ARX_Symb 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

the array of functions used to transform endogenous variables

fExo

the array of functions used to transform exogenous variables

fname_

the feature/variable names

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters

tForm

the transform for y

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