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scalation/scalation/scalation.modeling/scalation.modeling.forecasting/scalation.modeling.forecasting.nonlinear

scalation.modeling.forecasting.nonlinear

package scalation.modeling.forecasting.nonlinear

Members list

Type members

Classlikes

class NARX_SR_D(x: MatrixD, y: MatrixD, hh: Int, n_exo: Int, fname: Array[String], tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ..., tForms: TransformMap = ..., w_nl: VectorD = ...) extends ARX_D

The NARX_SR_D class provides time series analysis capabilities for NARX Symbolic Regression (SR) models with Direct (D) forecasting. These models include trend, linear, power, root, and cross terms for the single endogenous (y) variable and zero or more exogenous (xe) variables. Given time series data stored in vector y and matrix xe, its next value y_t = combination of last p values of y, y^p, y^r and the last q values of each exogenous variable xe_j, again in linear, power and root forms (as well as ENDO-EXO cross terms).

The NARX_SR_D class provides time series analysis capabilities for NARX Symbolic Regression (SR) models with Direct (D) forecasting. These models include trend, linear, power, root, and cross terms for the single endogenous (y) variable and zero or more exogenous (xe) variables. Given time series data stored in vector y and matrix xe, its next value y_t = combination of last p values of y, y^p, y^r and the last q values of each exogenous variable xe_j, again in linear, power and root forms (as well as ENDO-EXO cross terms).

y_t = b dot x_t + e_t

where y_t is the value of y at time t, x_t is a vector of inputs, and e_t is the residual/error term.

Value parameters

bakcast

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

fname

the feature/variable names

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to MakeMatrix4TS.hp)

n_exo

the number of exogenous variables

tForms

the map of transformations applied

tRng

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

w_nl

the non-linear parameters

x

the data/input matrix (lagged columns of y and xe) @see NARX_SR_D.apply

y

the response/output vector (main time series data)

Attributes

See also

MakeMatrix4TS for hyper-parameter specifications.

Companion
object
Supertypes
class ARX_D
class Forecaster_D
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object NARX_SR_D

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
NARX_SR_D.type
final class nARX_SR_DTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class nARX_SR_DTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class nARX_SR_DTest5

Attributes

Supertypes
class Object
trait Matchable
class Any

Value members

Concrete methods

def nARX_SR_DTest3(): Unit

The nARX_SR_DTest3 main function tests the NARX_SR_D class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The nARX_SR_DTest3 main function tests the NARX_SR_D class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.nonlinear.nARX_SR_DTest3

Attributes

def nARX_SR_DTest4(): Unit

The nARX_SR_DTest4 main function tests the NARX_SR_D class on real data: Forecasting COVID-19 using Train and Test (TnT). Test forecasts (h = 1 to hh steps ahead forecasts).

The nARX_SR_DTest4 main function tests the NARX_SR_D class on real data: Forecasting COVID-19 using Train and Test (TnT). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.nonlinear.nARX_SR_DTest4

Attributes

def nARX_SR_DTest5(): Unit

The nARX_SR_DTest5 main function fit the linear and non linear parameters and tests the NARX_SR_D class on real data: Forecasting COVID-19 using Train and Test (TnT). Test forecasts (h = 1 to hh steps ahead forecasts).

The nARX_SR_DTest5 main function fit the linear and non linear parameters and tests the NARX_SR_D class on real data: Forecasting COVID-19 using Train and Test (TnT). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.nonlinear.nARX_SR_DTest5

Attributes

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