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

scalation.modeling.forecasting

package scalation.modeling.forecasting

Members list

Packages

package scalation.modeling.forecasting.multivar
package scalation.modeling.forecasting.neuralforecasting

Type members

Classlikes

class AR(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean, adjusted: Boolean) extends Forecaster, Correlogram

The AR class provides basic time series analysis capabilities for Auto-Regressive (AR) models. AR models are often used for forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

The AR class provides basic time series analysis capabilities for Auto-Regressive (AR) models. AR models are often used for forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

y_t = b dot [1, y_t-1, ..., y_t-p) + e_t

where y_t is the value of y at time t and e_t is the residual/error term.

Value parameters

adjusted

whether in Correlogram when calculating auto-covarainces/auto-correlations to adjust to account for the number of elements in the sum Σ (or use dim-1)

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to AR.hp)

tRng

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

y

the response vector (time series data)

Attributes

See also

VectorD.acov

Companion
object
Supertypes
trait Correlogram
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARMA
class ARIMA
object AR

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
AR.type
class ARIMA(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends ARMA

The ARIMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models. In an ARIMA(p, d, q) model, p and q refer to the order of the Auto-Regressive and Moving-Average components of the model; d refers to the order of differencing. Given time series data stored in vector y, its next value y_t = y(t) may be predicted based on prior values of y and its noise:

The ARIMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models. In an ARIMA(p, d, q) model, p and q refer to the order of the Auto-Regressive and Moving-Average components of the model; d refers to the order of differencing. Given time series data stored in vector y, its next value y_t = y(t) may be predicted based on prior values of y and its noise:

y_t = δ + Σ(φ_i y_t-i) + Σ(θ_i e_t-i) + e_t

where δ is a constant, φ is the auto-regressive coefficient vector, θ is the moving-average coefficient vector, and e is the noise vector.

If d > 0, then the time series must be differenced first before applying the above model.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to AR.hp)

tRng

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

y

the response vector (time series data)

Attributes

Supertypes
class ARMA
class AR
trait Correlogram
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object ARIMA_diff

The ARIMA_diff object provides methods for taking first and second order differences, as well as transforming back to the original scale.

The ARIMA_diff object provides methods for taking first and second order differences, as well as transforming back to the original scale.

diff: position y --> velocity v --> acceleration a (actual) | | | backform, undiff: position yp <-- velocity vp <-- acceleration ap (predicted)

Attributes

See also

stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima

Supertypes
class Object
trait Matchable
class Any
Self type
ARIMA_diff.type
class ARMA(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends AR

The ARMA class provides basic time series analysis capabilities for Auto-Regressive, Moving Average (ARMA) models. ARMA models are often used for forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values and q shocks.

The ARMA class provides basic time series analysis capabilities for Auto-Regressive, Moving Average (ARMA) models. ARMA models are often used for forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values and q shocks.

y_t = δ + Σ[φ_j y_t-j] + Σ[θ_j e_t-j] + e_t

where y_t is the value of y at time t and e_t is the residual/error term.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to AR.hp)

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
class AR
trait Correlogram
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARIMA
object ARMA

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARMA.type
class ARX(x: MatrixD, y: VectorD, hh: Int, n_exo: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends Forecaster_Reg

The ARX class provides basic time series analysis capabilities for ARX models. ARX models build on ARY by including one or more exogenous (xe) variables. Given time series data stored in vector y, its next value y_t = combination of last p values of y and the last q values of each exogenous variable xe_j.

The ARX class provides basic time series analysis capabilities for ARX models. ARX models build on ARY by including one or more exogenous (xe) variables. Given time series data stored in vector y, its next value y_t = combination of last p values of y and the last q values of each exogenous variable xe_j.

y_t = b dot x_t + e_t

where y_t is the value of y at time t 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)

x

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

y

the response/output vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARX_Quad
class ARX_Symb
object ARX extends MakeMatrix4TS

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX.type
class ARX_D(x: MatrixD, y: MatrixD, hh: Int, n_exo: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends Forecaster_D

The ARX_D class provides basic time series analysis capabilities for ARX_D models. ARX_D models are often used for forecasting. ARX_D uses DIRECT (as opposed to RECURSIVE) multi-horizon forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

The ARX_D class provides basic time series analysis capabilities for ARX_D models. ARX_D models are often used for forecasting. ARX_D uses DIRECT (as opposed to RECURSIVE) multi-horizon forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

y_t = b dot x_t + e_t

where y_t is the value of y at time t 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)

x

the data/input matrix (lagged columns of y) @see ARX_D.apply

y

the response/output matrix (column per horizon) (time series data)

Attributes

Companion
object
Supertypes
class Forecaster_D
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARX_Quad_D
class ARX_Symb_D
object ARX_D extends MakeMatrix4TS

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX_D.type
class ARX_Quad(x: MatrixD, y: VectorD, hh: Int, n_exo: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends ARX

The ARX_Quad class provides basic time series analysis capabilities for ARX quadratic models. ARX quadratic models utilize quadratic multiple linear regression based on lagged values of y. ARX models build on ARY by including one or more exogenous (xe) variables. Given time series data stored in vector y, its next value y_t = combination of last p values of y, y^2 and the last q values of each exogenous variable xe_j.

The ARX_Quad class provides basic time series analysis capabilities for ARX quadratic models. ARX quadratic models utilize quadratic multiple linear regression based on lagged values of y. ARX models build on ARY by including one or more exogenous (xe) variables. Given time series data stored in vector y, its next value y_t = combination of last p values of y, y^2 and the last q values of each exogenous variable xe_j.

y_t = b dot x_t + e_t

where y_t is the value of y at time t 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)

x

the data/input matrix (lagged columns of y, y^2 and xe) @see ARX_Quad.apply

y

the response/output vector (time series data)

Attributes

Companion
object
Supertypes
class ARX
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object ARX_Quad extends MakeMatrix4TS

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX_Quad.type
class ARX_Quad_D(x: MatrixD, y: MatrixD, hh: Int, n_exo: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends ARX_D

The ARX_Quad_D class provides basic time series analysis capabilities for ARX_Quad_D models. ARX_Quad_D models are often used for forecasting. ARX_Quad_D uses DIRECT (as opposed to RECURSIVE) multi-horizon forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

The ARX_Quad_D class provides basic time series analysis capabilities for ARX_Quad_D models. ARX_Quad_D models are often used for forecasting. ARX_Quad_D uses DIRECT (as opposed to RECURSIVE) multi-horizon forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

y_t = b dot x_t + e_t

where y_t is the value of y at time t 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)

x

the data/input matrix (lagged columns of y) @see ARX_Quad_D.apply

y

the response/output matrix (column per horizon) (time series data)

Attributes

Companion
object
Supertypes
class ARX_D
class Forecaster_D
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object ARX_Quad_D extends MakeMatrix4TS

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX_Quad_D.type
class ARX_Symb(x: MatrixD, y: VectorD, hh: Int, n_exo: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends ARX

The ARX_Symb class provides time series analysis capabilities for ARX Symbolic Regression (SR) models. 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 ARX_Symb class provides time series analysis capabilities for ARX Symbolic Regression (SR) models. 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)

x

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

y

the response/output vector (main time series data)

Attributes

See also

MakeMatrix4TS for hyper-parameter specifications.

Companion
object
Supertypes
class ARX
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object ARX_Symb extends MakeMatrix4TS

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX_Symb.type
class ARX_Symb_D(x: MatrixD, y: MatrixD, hh: Int, n_exo: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends ARX_D

The ARX_Symb_D class provides time series analysis capabilities for ARX_D Symbolic Regression (SR) models. 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 ARX_Symb_D class provides time series analysis capabilities for ARX_D Symbolic Regression (SR) models. 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)

x

the data/input matrix (lagged columns of y and xe) @see ARX_Symb_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 Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object ARX_Symb_D extends MakeMatrix4TS

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX_Symb_D.type
class ARY(x: MatrixD, y: VectorD, hh: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends Forecaster_Reg

The ARY class provides basic time series analysis capabilities for ARY models. ARY models utilize multiple linear regression based on lagged values of y. Given time series data stored in vector y, its next value y_t = combination of last p values of y.

The ARY class provides basic time series analysis capabilities for ARY models. ARY models utilize multiple linear regression based on lagged values of y. Given time series data stored in vector y, its next value y_t = combination of last p values of y.

y_t = b dot x_t + e_t

where y_t is the value of y at time t 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)

tForms

the map of transformations applied

tRng

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

x

the data/input matrix (lagged columns of y) @see ARY.apply

y

the response/output vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARY_Quad
class SARY
object ARY extends MakeMatrix4TSY

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARY.type
class ARY_D(x: MatrixD, y: MatrixD, hh: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends Forecaster_D

The ARY_D class provides basic time series analysis capabilities for ARY_D models. ARY_D models are often used for forecasting. ARY_D uses DIRECT (as opposed to RECURSIVE) multi-horizon forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

The ARY_D class provides basic time series analysis capabilities for ARY_D models. ARY_D models are often used for forecasting. ARY_D uses DIRECT (as opposed to RECURSIVE) multi-horizon forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

y_t = b dot x_t + e_t

where y_t is the value of y at time t 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)

tForms

the map of transformations applied

tRng

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

x

the data/input matrix (lagged columns of y) @see ARY_D.apply

y

the response/output matrix (column per horizon) (time series data)

Attributes

Companion
object
Supertypes
class Forecaster_D
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object ARY_D extends MakeMatrix4TSY

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARY_D.type
class ARY_Quad(x: MatrixD, y: VectorD, hh: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean, tForms: TransformMap) extends ARY

The ARY_Quad class provides basic time series analysis capabilities for ARY quadratic models. ARY quadratic models utilize quadratic multiple linear regression based on lagged values of y. Given time series data stored in vector y, its next value y_t = combination of last p values of y and y^2.

The ARY_Quad class provides basic time series analysis capabilities for ARY quadratic models. ARY quadratic models utilize quadratic multiple linear regression based on lagged values of y. Given time series data stored in vector y, its next value y_t = combination of last p values of y and y^2.

y_t = b dot x_t + e_t

where y_t is the value of y at time t 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)

tForms

the map of transformations applied

tRng

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

x

the data/input matrix (lagged columns of y and y^2) @see ARY_Quad.apply

y

the response/output vector (time series data)

Attributes

Companion
object
Supertypes
class ARY
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object ARY_Quad extends MakeMatrix4TSY

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARY_Quad.type
class Baseline(y: VectorD, mtype: String) extends FitM

The Baseline class supports simple baseline time series models showing their In-Sample Testing in an easy to understand tabular format. One-step ahead forecasts are produced for all but the first time point (t = 0). Currently supports "NULL", "RW". "AR1", and "AR2".

The Baseline class supports simple baseline time series models showing their In-Sample Testing in an easy to understand tabular format. One-step ahead forecasts are produced for all but the first time point (t = 0). Currently supports "NULL", "RW". "AR1", and "AR2".

Value parameters

mtype

the type of model as a string

y

the time series vector

Attributes

See also

otexts.com/fpp3/acf.html for Auto-Correlation Function (ACF)

Supertypes
trait FitM
class Object
trait Matchable
class Any
class DTW(y: VectorD, q: Int)(ty: VectorT)

The DTW class is used for aligning two time series.

The DTW class is used for aligning two time series.

Value parameters

q

use the L_q norm (defaults to 2 (Euclidean))

ty

the corresponding date-time vector

y

the first time series vector

Attributes

Supertypes
class Object
trait Matchable
class Any
abstract class Diagnoser(dfm: Double, df: Double) extends Fit

The Diagnoser trait provides methods to determine basic Quality of Fit QoF measures.

The Diagnoser trait provides methods to determine basic Quality of Fit QoF measures.

Value parameters

df

the degrees of freedom for error

dfm

the degrees of freedom for model/regression (0 or more)

y_

the response vector (time series)

Attributes

Supertypes
trait Fit
trait FitM
class Object
trait Matchable
class Any
Known subtypes
class AR_Star
class VAR
class Forecaster
class AR
class ARMA
class ARIMA
class Forecaster_D
class ARX_D
class ARX_Quad_D
class ARX_Symb_D
class ARY_D
class ARX
class ARX_Quad
class ARX_Symb
class ARY
class ARY_Quad
class SARY
class NullModel
class RandomWalk
class RandomWalkS
class TranARY
class TrendModel
Show all
object Example_Covid

The Example_Covid object provides a convenient way to load Covid-19 weekly data. See test cases (odd In-ST, even TnT Split) below for Loss/Equations Optimizer (a: 1, 2) Plot and EDA - - Univariate: (b: 3, 4) Baseline Models none or CSSE none or various (c: 5, 6) AR(p) Models Yule-Walker Durbin-Levinson (d: 7, 8) ARMA(p, q=0) Models CSSE BFGS (e: 9, 10) ARY(p) Models CSSE QR Factorization (f: 11, 12) ARY_D(p) Models CSSE + Direct QR Factorization (g: 13, 14) ARMA(p, q=1) Models CSSE BFGS Multivariate: (h: 15, 16) ARX(p, 2, 2) Models CSSE QR Factorization (i: 17, 18) ARX_D Models CSSE + Direct QR Factorization (j: 19, 20) ARX_Quad_D Models CSSE QR Factorization

The Example_Covid object provides a convenient way to load Covid-19 weekly data. See test cases (odd In-ST, even TnT Split) below for Loss/Equations Optimizer (a: 1, 2) Plot and EDA - - Univariate: (b: 3, 4) Baseline Models none or CSSE none or various (c: 5, 6) AR(p) Models Yule-Walker Durbin-Levinson (d: 7, 8) ARMA(p, q=0) Models CSSE BFGS (e: 9, 10) ARY(p) Models CSSE QR Factorization (f: 11, 12) ARY_D(p) Models CSSE + Direct QR Factorization (g: 13, 14) ARMA(p, q=1) Models CSSE BFGS Multivariate: (h: 15, 16) ARX(p, 2, 2) Models CSSE QR Factorization (i: 17, 18) ARX_D Models CSSE + Direct QR Factorization (j: 19, 20) ARX_Quad_D Models CSSE QR Factorization

Known Bugs: 13, 14

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
object Example_GasFurnace

The Example_GasFurnace object provides a convenient way to load gas_furnace data.

The Example_GasFurnace object provides a convenient way to load gas_furnace data.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
object Example_ILI

The Example_ILI object provides a convenient way to load ILI weekly data.

The Example_ILI object provides a convenient way to load ILI weekly data.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
object Example_LakeLevels

The Example_LakeLevels provides a simple example time-series for testing forecasting models.

The Example_LakeLevels provides a simple example time-series for testing forecasting models.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
trait ForecastMatrix(y: VectorD, hh: Int, tRng: Range)

The ForecastMatrix trait provides a common framework for holding forecasts over time and multiple horizons.

The ForecastMatrix trait provides a common framework for holding forecasts over time and multiple horizons.

Value parameters

hh

the maximum forecasting horizon (h = 1 to hh)

tRng

the time vector, if relevant (index as time may suffice)

y

the response vector (time series data)

Attributes

Supertypes
class Object
trait Matchable
class Any
Known subtypes
class Forecaster
class AR
class ARMA
class ARIMA
class Forecaster_D
class ARX_D
class ARX_Quad_D
class ARX_Symb_D
class ARY_D
class ARX
class ARX_Quad
class ARX_Symb
class ARY
class ARY_Quad
class SARY
class NullModel
class RandomWalk
class RandomWalkS
class TranARY
class TrendModel
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abstract class Forecaster(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Diagnoser, ForecastMatrix, Model

The Forecaster abstract class provides a common framework for several forecasters. Note, the train method must be called first followed by test.

The Forecaster abstract class provides a common framework for several forecasters. Note, the train method must be called first followed by test.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters for models extending this abstract class

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class AR
class ARMA
class ARIMA
class Forecaster_D
class ARX_D
class ARX_Quad_D
class ARX_Symb_D
class ARY_D
class ARX
class ARX_Quad
class ARX_Symb
class ARY
class ARY_Quad
class SARY
class NullModel
class RandomWalk
class RandomWalkS
class TranARY
class TrendModel
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object Forecaster

The Forecaster companion object provides methods useful for classes extending the Forecaster abstract class, i.e., forecasting models with a single input variable.

The Forecaster companion object provides methods useful for classes extending the Forecaster abstract class, i.e., forecasting models with a single input variable.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
Forecaster.type
abstract class Forecaster_D(x: MatrixD, y: MatrixD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster

The Forecaster_D abstract class provides a common framework for several forecasters. Note, the train_x method must be called first followed by test.

The Forecaster_D abstract class provides a common framework for several forecasters. Note, the train_x method must be called first followed by test.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters for models extending this abstract class

tRng

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

x

the input lagged time series data

y

the response matrix (time series data per horizon)

Attributes

Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARX_D
class ARX_Quad_D
class ARX_Symb_D
class ARY_D
abstract class Forecaster_Reg(x: MatrixD, y: VectorD, hh: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster, FeatureSelection

The Forecaster_Reg abstract class provides base methods for use by extending classes that utilize regression for time series forecasting.

The Forecaster_Reg abstract class provides base methods for use by extending classes that utilize regression for time series forecasting.

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)

tRng

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

x

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

y

the response/output vector (time series data)

Attributes

Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARX
class ARX_Quad
class ARX_Symb
class ARY
class ARY_Quad
class SARY
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trait MakeMatrix4TS

The MakeMatrix4TS trait provides factory method templates for for invoking ARX* constructors.

The MakeMatrix4TS trait provides factory method templates for for invoking ARX* constructors.

Attributes

Companion
object
Supertypes
class Object
trait Matchable
class Any
Known subtypes
object ARX
object ARX_D
object ARX_Quad
object ARX_Quad_D
object ARX_Symb
object ARX_Symb_D
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object MakeMatrix4TS

The MakeMatrix4TS object provides methods for making/building matrices from lagged endogenous and exogenous variables.

The MakeMatrix4TS object provides methods for making/building matrices from lagged endogenous and exogenous variables.

Attributes

Companion
trait
Supertypes
class Object
trait Matchable
class Any
Self type
trait MakeMatrix4TSY

The MakeMatrix4TSY trait provides factory method templates for for invoking ARY* constructors.

The MakeMatrix4TSY trait provides factory method templates for for invoking ARY* constructors.

Attributes

Supertypes
class Object
trait Matchable
class Any
Known subtypes
object ARY
object ARY_D
object ARY_Quad
class NullModel(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster

The NullModel class provides basic time series analysis capabilities for NullModel models. NullModel models are often used for forecasting. Given time series data stored in vector y, its next value y_t = mean may be predicted based on its past value of y:

The NullModel class provides basic time series analysis capabilities for NullModel models. NullModel models are often used for forecasting. Given time series data stored in vector y, its next value y_t = mean may be predicted based on its past value of y:

y_t = mean + e_t

where mean is the mean of y and e_t is the new residual/error term.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (none => use null)

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
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object NullModel

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
NullModel.type
class Periodogram(y: VectorD)

The Periodogram class is used to analyze the frequency specturm of a time serioes.

The Periodogram class is used to analyze the frequency specturm of a time serioes.

Value parameters

y

the first time series vector

Attributes

Supertypes
class Object
trait Matchable
class Any
class RandomWalk(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster

The RandomWalk class provides basic time series analysis capabilities for RandomWalk models. RandomWalk models are often used for forecasting. Given time series data stored in vector y, its next value y_t+1 = y(t+1) may be predicted based on its past value of y:

The RandomWalk class provides basic time series analysis capabilities for RandomWalk models. RandomWalk models are often used for forecasting. Given time series data stored in vector y, its next value y_t+1 = y(t+1) may be predicted based on its past value of y:

y_t+1 = y_t + e_t+1

where y_t is the previous value of y and e_t+1 is the new residual/error term.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (none => use null)

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object RandomWalk

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
RandomWalk.type
class RandomWalkS(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster

The RandomWalkS class provides basic time series analysis capabilities for RandomWalkS models. RandomWalkS models are often used for forecasting. Given time series data stored in vector y, its next value y_t is the previous value adjusted by the slope weighted by s.

The RandomWalkS class provides basic time series analysis capabilities for RandomWalkS models. RandomWalkS models are often used for forecasting. Given time series data stored in vector y, its next value y_t is the previous value adjusted by the slope weighted by s.

y_t = y_t-1 + s (y_t-1 - y_t-2) + e_t

where y_t is the value of y at time t and e_t is the residual/error term.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to RandomWalkS.hp)

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object RandomWalkS

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
class SARY(x: MatrixD, y: VectorD, hh: Int, fname: Array[String], tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends ARY

The SARY class provides basic time series analysis capabilities for SARY models. SARY models utilize multiple linear regression based on lagged values of y. Given time series data stored in vector y, its next value y_t = combination of last p values of y.

The SARY class provides basic time series analysis capabilities for SARY models. SARY models utilize multiple linear regression based on lagged values of y. Given time series data stored in vector y, its next value y_t = combination of last p values of y.

y_t = b dot x_t + e_t

where y_t is the value of y at time t 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)

tRng

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

x

the data/input matrix (lagged columns of y) @see SARY.apply

y

the response/output vector (time series data)

Attributes

Companion
object
Supertypes
class ARY
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object SARY

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
SARY.type
class SimpleExpSmoothing(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster

The SimpleExpSmoothing class provides basic time series analysis capabilities for Simple Exponential Smoothing models. SimpleExpSmoothing models are often used for forecasting. Given time series data stored in vector y, its next value y_t = mean of last q values.

The SimpleExpSmoothing class provides basic time series analysis capabilities for Simple Exponential Smoothing models. SimpleExpSmoothing models are often used for forecasting. Given time series data stored in vector y, its next value y_t = mean of last q values.

s_t  = α y_t-1 + (1 - α) s_t-1       smoothing equation
yf_t = s_t                           forecast equation

where vector s is the smoothed version of vector y.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to SimpleExpSmoothing.hp)`

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object SimpleExpSmoothing

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
class SimpleMovingAverage(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster

The SimpleMovingAverage class provides basic time series analysis capabilities for SimpleMovingAverage models. SimpleMovingAverage models are often used for forecasting. Given time series data stored in vector y, its next value y_t = mean of last q values.

The SimpleMovingAverage class provides basic time series analysis capabilities for SimpleMovingAverage models. SimpleMovingAverage models are often used for forecasting. Given time series data stored in vector y, its next value y_t = mean of last q values.

y_t = mean (y_t-1, ..., y_t-q) + e_t

where y_t is the value of y at time t and e_t is the residual/error term.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to SimpleMovingAverage.hp)

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object SimpleMovingAverage

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
object Stationarity_KPSS

The companion object for KPSS class, containing critical value coefficients needed in KPSS tests for Time Series Stationarity around a deterministic trend.

The companion object for KPSS class, containing critical value coefficients needed in KPSS tests for Time Series Stationarity around a deterministic trend.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
class Stationarity_KPSS(yy: VectorD, lags_: Int, lagsType_: String, trend_: String) extends UnitRoot

The KPSS class provides capabilities of performing KPSS test to determine if a time series is stationary around a deterministic trend. This code is translated from the C++ code found in

The KPSS class provides capabilities of performing KPSS test to determine if a time series is stationary around a deterministic trend. This code is translated from the C++ code found in

Value parameters

lagsType_

type of lags, long or short

lags_

the number of lags to use

trend_

type of trend to test for

yy

the original time series vector

Attributes

See also

github.com/olmallet81/URT.

Companion
object
Supertypes
trait UnitRoot
class Object
trait Matchable
class Any
class TranARY(x: MatrixD, y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, val itran: FunctionS2S, bakcast: Boolean) extends Forecaster

The TranARY class provides basic time series analysis capabilities for TranARY models. TranARY models are often used for forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

The TranARY class provides basic time series analysis capabilities for TranARY models. TranARY models are often used for forecasting. Given time series data stored in vector y, its next value y_t = combination of last p values.

tran (y_t) = b dot x_t + e_t

where y_t is the value of y at time t and e_t is the residual/error term.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to MakeMatrix4TS.hp)

itran

the inverse transformation to return to the original scale (defaults to expm1)

tRng

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

x

the data/input matrix (lagged columns of y) @see ARY.apply

y

the response/output vector (time series data)

Attributes

See also

TranARY.apply for applying transformations (tran, itran)

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object TranARY

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
TranARY.type
class TrendModel(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster

The TrendModel class provides basic time series analysis capabilities for TrendModel models. TrendModel models are often used for forecasting. Given time series data stored in vector y, its next value y_t = f(t) = b_0 + b_1 t.

The TrendModel class provides basic time series analysis capabilities for TrendModel models. TrendModel models are often used for forecasting. Given time series data stored in vector y, its next value y_t = f(t) = b_0 + b_1 t.

y_t = f(t) + e_t

where y_t is the value of y at time t and e_t is the residual/error term.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (none => use null)

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object TrendModel

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
TrendModel.type
trait UnitRoot(val testName: String, val nobs: Int, val validTrends: VectorS, var lagsType: String, var lags: Int, var trend: String)

The UnitRoot trait provides a common framework for various unit root testers for Time Series Stationarity. This code is translated from the C++ code found in

The UnitRoot trait provides a common framework for various unit root testers for Time Series Stationarity. This code is translated from the C++ code found in

Value parameters

lags

the number of lags to use

lagsType

default lags value long or short time-series

nobs

the number of observations (length of time-series)

testName

the name of test, e.g., KPSS

trend

type of trend to test for

validTrends

vector of test valid trends types, e.g., constant, linear trend

Attributes

See also

github.com/olmallet81/URT.

Supertypes
class Object
trait Matchable
class Any
Known subtypes
class WeightedMovingAverage(y: VectorD, hh: Int, tRng: Range, hparam: HyperParameter, bakcast: Boolean) extends Forecaster

The WeightedMovingAverage class provides basic time series analysis capabilities for WeightedMovingAverage models. WeightedMovingAverage models are often used for forecasting. Given time series data stored in vector y, its next value y_t = weighted mean of last q values.

The WeightedMovingAverage class provides basic time series analysis capabilities for WeightedMovingAverage models. WeightedMovingAverage models are often used for forecasting. Given time series data stored in vector y, its next value y_t = weighted mean of last q values.

y_t = weighted-mean (y_t-1, ..., y_t-q) + e_t

where y_t is the value of y at time t and e_t is the residual/error term.

Value parameters

bakcast

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

hh

the maximum forecasting horizon (h = 1 to hh)

hparam

the hyper-parameters (defaults to SimpleMovingAverage.hp)

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
class Forecaster
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object WeightedMovingAverage

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
final class aRIMATest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRIMATest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRIMATest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRIMA_diffTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRMATest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRMATest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRMATest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRMATest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRMATest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRMATest6

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRMATest7

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRTest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRXTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRXTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRXTest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_DTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_DTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_QuadTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_QuadTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_QuadTest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_Quad_DTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_Quad_DTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_SymbTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_SymbTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_Symb_DTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRX_Symb_DTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRYTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRYTest2

Attributes

Supertypes
class Object
trait Matchable
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final class aRYTest3

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final class aRY_DTest

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final class aRY_DTest3

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final class aRY_DTest4

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final class aRY_QuadTest

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final class aRY_QuadTest5

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final class baselineTest

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final class dTWTest

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final class example_CovidTest

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final class example_CovidTest10

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final class example_CovidTest11

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final class example_CovidTest12

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final class example_CovidTest15

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final class example_CovidTest16

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final class example_CovidTest2

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final class example_CovidTest3

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final class example_CovidTest4

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final class example_CovidTest5

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final class example_CovidTest6

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final class example_CovidTest7

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final class example_CovidTest8

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final class example_CovidTest9

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final class example_GasFurnaceTest

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final class example_GasFurnaceTest2

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final class example_ILITest

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final class example_ILITest10

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final class example_ILITest2

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final class example_ILITest3

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final class example_ILITest5

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final class example_ILITest6

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final class example_ILITest8

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final class example_ILITest9

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final class forecastMatrixTest

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final class forecastMatrixTest2

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final class forecastMatrixTest3

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final class forecastMatrixTest4

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final class nullModelTest

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final class nullModelTest2

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final class nullModelTest3

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final class nullModelTest4

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final class nullModelTest5

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final class periodogramTest

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final class randomWalkSTest

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final class randomWalkSTest2

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final class randomWalkSTest3

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final class randomWalkSTest4

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final class randomWalkTest

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final class randomWalkTest2

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final class randomWalkTest3

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final class randomWalkTest4

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final class randomWalkTest5

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final class randomWalkTest6

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final class sARYTest

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final class sARYTest2

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final class sARYTest3

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final class sARYTest4

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final class sARYTest5

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final class simpleExpSmoothingTest

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final class simpleExpSmoothingTest2

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final class simpleExpSmoothingTest3

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final class simpleExpSmoothingTest4

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final class simpleMovingAverageTest

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final class simpleMovingAverageTest2

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final class simpleMovingAverageTest3

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final class simpleMovingAverageTest4

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final class stationarity_KPSSTest

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final class stationarity_KPSSTest2

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final class stationarity_KPSSTest3

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final class stationaryTest

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final class stationaryTest2

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final class stationaryTest3

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final class tranARYTest

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final class tranARYTest2

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final class tranARYTest3

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final class tranARYTest4

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final class trendModelTest

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final class trendModelTest2

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final class trendModelTest3

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final class trendModelTest4

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final class weightedMovingAverageTest

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final class weightedMovingAverageTest2

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final class weightedMovingAverageTest3

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final class weightedMovingAverageTest4

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trait Matchable
class Any

Types

type CriticalValues = HashMap[Int, VectorD]
type TransformMap = Map[String, Transform | Array[Transform]]

The TransformMap type and its extension methods provides maps of transforms.

The TransformMap type and its extension methods provides maps of transforms.

Attributes

Value members

Concrete methods

def aRIMATest(): Unit

The aRIMATest main function tests the ARIMA class on simulated data. Test predictions (one step ahead forecasts).

The aRIMATest main function tests the ARIMA class on simulated data. Test predictions (one step ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRIMATest

def aRIMATest2(): Unit

The aRIMATest2 main function tests the ARIMA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts) with no differencing

The aRIMATest2 main function tests the ARIMA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts) with no differencing

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRIMATest2

def aRIMATest3(): Unit

The aRIMATest3 main function tests the ARIMA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts) taking one difference.

The aRIMATest3 main function tests the ARIMA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts) taking one difference.

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRIMATest3

def aRIMA_diffTest(): Unit

The aRIMA_diffTest main function tests the ARIMA_diff object on real data: Forecasting lake levels comparing ARMA, AR1MA, Differenced ARMA, Transformed-Back Differenced ARMA. Observe that backform is better than undiff on predictions.

The aRIMA_diffTest main function tests the ARIMA_diff object on real data: Forecasting lake levels comparing ARMA, AR1MA, Differenced ARMA, Transformed-Back Differenced ARMA. Observe that backform is better than undiff on predictions.

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRIMA_diffTest

def aRMATest(): Unit

The aRMATest main function tests the ARMA class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRMATest main function tests the ARMA class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRMATest

def aRMATest2(): Unit

The aRMATest2 main function tests the ARMA class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRMATest2 main function tests the ARMA class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRMATest2

def aRMATest3(): Unit

The aRMATest3 main function tests the ARMA class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). Comparison of sMAPE for AR(p), ARY(p), ARY_D(p), ARMA(p, 0), and ARMA(p, 1). Note ARX (p, 1, 0), where 0 => no exo vars, duplicates results of ARY(p)

The aRMATest3 main function tests the ARMA class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). Comparison of sMAPE for AR(p), ARY(p), ARY_D(p), ARMA(p, 0), and ARMA(p, 1). Note ARX (p, 1, 0), where 0 => no exo vars, duplicates results of ARY(p)

19.0371, 29.5797, 39.0740, 47.4638, 55.1785, 62.1818 RW

18.7298, 28.4908, 37.0997, 45.6487, 51.7248, 56.3708 AR(1) 18.5808, 28.3362, 37.2485, 45.7846, 52.0362, 56.9114 ARY(1) 18.5808, 28.8144, 37.7469, 44.8006, 49.8166, 52.3205 ARY_D(1) 18.5788, 28.3364, 37.2530, 45.7883, 52.0403, 56.9181 ARY_Quad(1) 18.7095, 28.4690, 37.1203, 45.6688, 51.7687, 56.4467 ARMA(1, 0) 17.0508, 26.4669, 35.4906, 43.5707, 49.4949, 54.2347 ARMA(1, 1)

16.3579, 24.7155, 33.0480, 40.0707, 46.0049, 50.8265 AR(2) 16.2270, 23.3708, 31.6615, 38.7385, 44.7630, 50.0814 ARY(2) 16.2270, 22.9698, 30.0933, 35.4960, 40.7977, 46.2700 ARY_D(2) 16.2663, 22.6643, 31.0768, 37.7388, 44.2476, 50.0283 ARY_Quad(2) 19.0826, 29.2723, 37.2914, 44.2636, 49.8307, 53.6992 ARMA(2, 0) 17.0445, 26.6538, 35.5239, 42.9937, 48.7679, 53.3489 ARMA(2, 1)

16.0114, 22.7408, 29.5631, 35.2773, 40.9870, 45.8408 AR(3) 15.7509, 21.9972, 28.8976, 34.6815, 40.7375, 46.1590 ARY(3) 15.7509, 21.8745, 28.2745, 32.9840, 39.1694, 43.9673 ARY_D(3) 15.7262, 21.2578, 28.4101, 34.1532, 40.6659 46.1492 ARY_Quad(3) 16.7027, 23.4111, 30.5995, 36.7396, 42.6680, 47.1189 ARMA(3, 0) 16.1750, 23.1243, 30.8535, 37.1636, 43.0417, 48.2946 ARMA(3, 1)

15.8988, 22.5738, 28.5298, 33.3360, 39.1586, 43.1606 AR(4) 15.6423, 21.7982, 27.9006, 33.1000, 39.0543, 43.9748 ARY(5) 15.6423, 21.8663, 28.0034, 32.9898, 38.9927, 43.6218 ARY_D(4) 15.5814, 21.2352, 28.5489, 34.4369, 40.3618, 45.2605 ARY_Quad(4) 16.6457, 22.9684, 29.0629, 34.6601, 40.1521, 44.0896 ARMA(4, 0) 15.3290, 21.9965, 27.8397, 34.3507, 40.0857, 45.8402 ARMA(4, 1)

15.9279, 22.5769, 28.5035, 33.3019, 39.1381, 43.0520 AR(5) 15.6349, 21.8003, 27.9084, 33.1127, 39.0628, 44.0175 ARY(5) 15.6349, 21.7885, 28.0114, 33.0117, 39.1418, 43.7715 ARY_D(5) 15.3209, 21.3541, 28.9325, 35.1359, 41.0300, 45.8558 ARY_Quad(5) 16.3720, 22.8047, 28.7702, 33.9232, 39.5677, 43.2628 ARMA(5, 0) 15.3361, 21.9121, 27.6568, 34.0218, 39.6254, 45.2994 ARMA(5, 1)

runMain scalation.modeling.forecasting.aRMATest3

Attributes

def aRMATest4(): Unit

The aRMATest4 main function tests the ARMA class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts). Comparison of sMAPE for AR(p), ARY(p), ARY_D(p), ARMA(p, 0), and ARMA(p, 1).

The aRMATest4 main function tests the ARMA class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts). Comparison of sMAPE for AR(p), ARY(p), ARY_D(p), ARMA(p, 0), and ARMA(p, 1).

19.1334, 31.1906, 44.3787, 55.1576, 65.1810, 74.0524 AR(1) 19.0397, 30.4570, 43.9113, 54.9642, 65.3163, 74.2124 ARY(1) 19.1718, 30.7038, 44.5265, 55.7794, 66.3876, 75.6566 ARMA(1, 0) 18.3012, 29.3224, 43.0369, 54.5719, 64.9230, 74.2520 ARMA(1, 1)

16.6447, 26.9109, 39.8106, 50.8595, 60.2176, 68.6317 AR(2) 16.8833, 26.4824, 39.2329, 50.8677, 61.0624, 70.3218 ARY(2) 19.4256, 32.8815, 46.4279, 57.2199, 66.8651, 75.3077 ARMA(2, 0) 18.3009, 30.0443, 43.6634, 54.9669, 64.8541, 73.7911 ARMA(2, 1)

15.9232, 23.5929, 34.3577, 44.1784, 53.6513, 62.0129 AR(3) 15.7190, 21.7959, 32.1395, 42.0074, 52.6874, 62.7276 ARY(3) 16.4547, 24.4668, 36.8597, 46.7958, 58.3539, 67.6623 ARMA(3, 0) 17.0353, 24.0309, 36.6585, 46.1961, 57.6348, 67.2332 ARMA(3, 1)

15.3256, 22.6893, 30.7558, 39.6274, 48.6646, 56.7375 AR(4) 14.6791, 19.9940, 26.5644, 35.4590, 41.4955, 50.8660 ARY(4) 14.9687, 22.2599, 29.6359, 39.6018, 48.2853, 56.9797 ARMA(4, 0) 15.2243, 21.4976, 27.7929, 37.9923, 45.0999, 54.3417 ARMA(4, 1)

15.9166, 21.5246, 28.0675, 36.8669, 43.3785, 51.1786 AR(5) 15.0232, 19.4222, 27.1981, 35.4744, 40.3466, 48.4066 ARY(5) 15.5426, 21.0405, 29.1731, 37.8006, 43.3590, 52.6387 ARMA(5, 0) 15.7641, 21.0723, 28.7463, 37.7968, 42.8480, 52.8277 ARMA(5, 1)

runMain scalation.modeling.forecasting.aRMATest4

Attributes

def aRMATest5(): Unit

The aRMATest5 main function tests the ARMA class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). Comparison of sMAPE for ARMA(p, 1) (i.e., q = 1) for different p orders.

The aRMATest5 main function tests the ARMA class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). Comparison of sMAPE for ARMA(p, 1) (i.e., q = 1) for different p orders.

runMain scalation.modeling.forecasting.aRMATest5

Attributes

def aRMATest6(): Unit

The aRMATest6 main function tests the ARMA class on small dataset. Test forecasts (h = 1 step ahead forecasts).

The aRMATest6 main function tests the ARMA class on small dataset. Test forecasts (h = 1 step ahead forecasts).

runMain scalation.modeling.forecasting.aRMATest6

Attributes

def aRMATest7(): Unit

The aRMATest7 main function tests the ARMA class on small dataset. Test the generation of ARMA sequences for various p and q values.

The aRMATest7 main function tests the ARMA class on small dataset. Test the generation of ARMA sequences for various p and q values.

runMain scalation.modeling.forecasting.aRMATest7

Attributes

def aRTest(): Unit

The aRTest main function tests the AR class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRTest main function tests the AR class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRTest

def aRTest2(): Unit

The aRTest2 main function tests the AR class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRTest2 main function tests the AR class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRTest2

def aRTest3(): Unit

The aRTest3 main function tests the AR class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRTest3 main function tests the AR 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.aRTest3

Attributes

def aRTest4(): Unit

The aRTest4 main function tests the AR class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRTest4 main function tests the AR class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRTest4

Attributes

def aRTest5(): Unit

The aRTest5 main function tests the AR class on small dataset. Test forecasts (h = 1 step ahead forecasts).

The aRTest5 main function tests the AR class on small dataset. Test forecasts (h = 1 step ahead forecasts).

runMain scalation.modeling.forecasting.aRTest5

Attributes

def aRXTest3(): Unit

The aRXTest3 main function tests the ARX class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRXTest3 main function tests the ARX 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.aRXTest3

Attributes

def aRXTest4(): Unit

The aRXTest4 main function tests the ARX class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRXTest4 main function tests the ARX class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRXTest4

Attributes

def aRXTest5(): Unit

The aRXTest5 main function tests the ARX class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

The aRXTest5 main function tests the ARX class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

runMain scalation.modeling.forecasting.aRXTest5

Attributes

def aRX_DTest3(): Unit

The aRX_DTest3 main function tests the ARX_D class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_DTest3 main function tests the ARX_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.aRX_DTest3

Attributes

def aRX_DTest4(): Unit

The aRX_DTest4 main function tests the ARX_D class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_DTest4 main function tests the ARX_D class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRX_DTest4

Attributes

def aRX_QuadTest3(): Unit

The aRX_QuadTest3 main function tests the ARX_Quad class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_QuadTest3 main function tests the ARX_Quad 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.aRX_QuadTest3

Attributes

def aRX_QuadTest4(): Unit

The aRX_QuadTest4 main function tests the ARX_Quad class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_QuadTest4 main function tests the ARX_Quad class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRX_QuadTest4

Attributes

def aRX_QuadTest5(): Unit

The aRX_QuadTest5 main function tests the ARX_Quad class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

The aRX_QuadTest5 main function tests the ARX_Quad class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

runMain scalation.modeling.forecasting.aRX_QuadTest5

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def aRX_Quad_DTest3(): Unit

The aRX_Quad_DTest3 main function tests the ARX_Quad_D class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_Quad_DTest3 main function tests the ARX_Quad_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.aRX_Quad_DTest3

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def aRX_Quad_DTest4(): Unit

The aRX_Quad_DTest4 main function tests the ARX_Quad_D class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_Quad_DTest4 main function tests the ARX_Quad_D class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRX_Quad_DTest4

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def aRX_SymbTest3(): Unit

The aRX_SymbTest3 main function tests the ARX_Symb class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_SymbTest3 main function tests the ARX_Symb 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.aRX_SymbTest3

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def aRX_SymbTest4(): Unit

The aRX_SymbTest4 main function tests the ARX_Symb class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_SymbTest4 main function tests the ARX_Symb class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRX_SymbTest4

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def aRX_Symb_DTest3(): Unit

The ARX_Symb_DTest3 main function tests the ARX_Symb_D class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The ARX_Symb_DTest3 main function tests the ARX_Symb_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.aRX_Symb_DTest3

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def aRX_Symb_DTest4(): Unit

The ARX_Symb_DTest4 main function tests the ARX_Symb_D class on real data: Forecasting COVID-19 using Train and Test (TnT). Test forecasts (h = 1 to hh steps ahead forecasts).

The ARX_Symb_DTest4 main function tests the ARX_Symb_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.aRX_Symb_DTest4

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def aRYTest(): Unit

The aRYTest main function tests the ARY class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRYTest main function tests the ARY class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRYTest

def aRYTest2(): Unit

The aRYTest2 main function tests the ARY class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRYTest2 main function tests the ARY class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRYTest2

def aRYTest3(): Unit

The aRYTest3 main function tests the ARY class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRYTest3 main function tests the ARY 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.aRYTest3

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def aRYTest4(): Unit

The aRYTest4 main function tests the ARY class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRYTest4 main function tests the ARY class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRYTest4

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def aRYTest5(): Unit

The aRYTest5 main function tests the ARY class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

The aRYTest5 main function tests the ARY class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

runMain scalation.modeling.forecasting.aRYTest5

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def aRYTest6(): Unit

The aRYTest6 main function tests the ARY object's ability to make/build input matrices. Build an input/predictor data matrix for the COVID-19 dataset.

The aRYTest6 main function tests the ARY object's ability to make/build input matrices. Build an input/predictor data matrix for the COVID-19 dataset.

runMain scalation.modeling.forecasting.aRYTest6

Attributes

def aRY_DTest(): Unit

The aRY_DTest main function tests the ARY_D class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRY_DTest main function tests the ARY_D class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRY_DTest

def aRY_DTest2(): Unit

The aRY_DTest2 main function tests the ARY_D class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRY_DTest2 main function tests the ARY_D class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRY_DTest2

def aRY_DTest3(): Unit

The aRY_DTest3 main function tests the ARY_D class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRY_DTest3 main function tests the ARY_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.aRY_DTest3

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def aRY_DTest4(): Unit

The aRY_DTest4 main function tests the ARY_D class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRY_DTest4 main function tests the ARY_D class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRY_DTest4

Attributes

def aRY_QuadTest(): Unit

The aRY_QuadTest main function tests the ARY_Quad class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRY_QuadTest main function tests the ARY_Quad class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRY_QuadTest

def aRY_QuadTest2(): Unit

The aRY_QuadTest2 main function tests the ARY_Quad class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRY_QuadTest2 main function tests the ARY_Quad class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.aRY_QuadTest2

def aRY_QuadTest3(): Unit

The aRY_QuadTest3 main function tests the ARY_Quad class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRY_QuadTest3 main function tests the ARY_Quad 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.aRY_QuadTest3

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def aRY_QuadTest4(): Unit

The aRY_QuadTest4 main function tests the ARY_Quad class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The aRY_QuadTest4 main function tests the ARY_Quad class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRY_QuadTest4

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def aRY_QuadTest5(): Unit

The aRY_QuadTest5 main function tests the ARY_Quad class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

The aRY_QuadTest5 main function tests the ARY_Quad class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

runMain scalation.modeling.forecasting.aRY_QuadTest5

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def baselineTest(): Unit

The baselineTest main function is used to test the Baseline class. It can performs Null, RW, AR(1), or AR(2) time series model calculations.

The baselineTest main function is used to test the Baseline class. It can performs Null, RW, AR(1), or AR(2) time series model calculations.

runMain scalation.modeling.forecasting.baselineTest

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def dTWTest(): Unit

The dTWTest main function tests the DTW class on real data.

The dTWTest main function tests the DTW class on real data.

runMain scalation.modeling.forecasting.dTWTest

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def example_CovidTest(): Unit

The example_CovidTest main function tests the Example_Covid object. Prints and plots the response column ("new_deaths").

The example_CovidTest main function tests the Example_Covid object. Prints and plots the response column ("new_deaths").

runMain scalation.modeling.forecasting.example_CovidTest

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def example_CovidTest10(): Unit

The example_CovidTest10 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Lagged Regression ARY(p) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest10 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Lagged Regression ARY(p) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

19.0003, 30.3936, 43.8008, 54.8254, 65.3736, 74.5465 ARY(1) 16.8486, 26.3959, 39.1085, 50.6966, 61.0053, 70.3446 ARY(2) 15.7448, 21.8608, 31.3677, 40.9140, 51.5319, 61.5140 ARY(3) 14.7953, 20.1791, 26.5422, 35.2717, 40.7200, 48.6407 ARY(4) 14.9856, 19.5241, 27.1485, 35.1070, 40.1716, 47.1898 ARY(5) 15.0238, 21.1032, 28.4153, 36.6326, 42.5539, 49.8734 ARY(6) 15.5620, 20.7860, 29.8501, 37.1646, 43.7716, 48.4778 ARY(7) 15.1719, 23.2761, 32.2952, 40.3584, 46.1975, 51.3488 ARY(8) 14.9497, 22.5065, 31.3207, 39.5034, 45.5495, 51.4103 ARY(9) 14.4824, 21.5906, 29.9550, 37.9214, 43.3013, 52.2868 ARY(10)

FIX - discrepancy between rollValidate and diagnoseAll handled by sft parameter - why needed?

runMain scalation.modeling.forecasting.example_CovidTest10

Attributes

def example_CovidTest11(): Unit

The example_CovidTest11 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Lagged Regression, Direct ARY_D(p) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest11 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Lagged Regression, Direct ARY_D(p) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

19.9912, 30.1349, 38.7483, 45.1096, 49.5424, 52.5320 ARY_D(1) 17.7245, 24.2871, 31.1716, 35.9357, 40.5132, 46.4806 ARY_D(2) 17.2367, 23.2007, 29.4120, 33.5757, 38.8647, 44.1707 ARY_D(3) 17.1336, 23.1984, 29.1758, 33.5773, 38.6493, 43.8045 ARY_D(4) 17.1196, 23.1224, 29.1769, 33.6120, 38.7839, 43.9346 ARY_D(5) 17.1324, 23.1273, 29.2292, 33.8956, 39.1209, 44.0869 ARY_D(6) 16.9815, 23.2879, 29.2536, 33.9433, 39.1474, 44.2361 ARY_D(7) 17.0492, 23.1888, 29.2826, 34.0878, 39.2379, 44.7474 ARY_D(8) 16.9841, 23.1090, 29.2154, 34.1249, 39.2711, 44.7709 ARY_D(9) 17.0676, 23.1089, 28.9425, 33.9046, 38.9082, 44.0469 ARY_D(10)

runMain scalation.modeling.forecasting.example_CovidTest11

Attributes

def example_CovidTest12(): Unit

The example_CovidTest12 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Lagged Regression, Direct ARY_D(p) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest12 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Lagged Regression, Direct ARY_D(p) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

18.9312, 31.2905, 45.7578, 57.0037, 65.9690, 72.4626 ARY_D(1) 16.8059, 23.1653, 31.9736, 40.6603, 46.6809, 57.1835 ARY_D(2) 15.9031, 20.7335, 27.3975, 35.5557, 39.3269, 51.2769 ARY_D(3) 15.0132, 20.2209, 27.5774, 35.4134, 39.7899, 48.6745 ARY_D(4) 15.2338, 19.4826, 27.6054, 35.6699, 39.8746, 48.4355 ARY_D(5) 15.1603, 19.7425, 27.7367, 35.7799, 40.1055, 49.1122 ARY_D(6) 15.5484, 22.7247, 31.0076, 38.5501, 44.5176, 50.8537 ARY_D(7) 15.3248, 23.2628, 30.6794, 39.0621, 44.5661, 52.6579 ARY_D(8) 15.0875, 21.7912, 30.2152, 37.4165, 42.6637, 52.9831 ARY_D(9) 14.7569, 22.2172, 30.9435, 40.5641, 46.2016, 57.6445 ARY_D(10)

runMain scalation.modeling.forecasting.example_CovidTest12

Attributes

def example_CovidTest13(): Unit

The example_CovidTest13 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Moving Average ARMA(p, q) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest13 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Moving Average ARMA(p, q) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

FIX - good for h = 1, but then sMAPE scores explode

runMain scalation.modeling.forecasting.example_CovidTest13

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def example_CovidTest14(): Unit

The example_CovidTest14 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Moving Average ARMA(p, q) models for several p values. and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest14 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Moving Average ARMA(p, q) models for several p values. and horizons 1 to 6, see sMAPE metrics below:

FIX - for all h sMAPE scores have exploded

runMain scalation.modeling.forecasting.example_CovidTest14

Attributes

def example_CovidTest15(): Unit

The example_CovidTest15 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Exogenous ARX(p, q, n) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest15 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Exogenous ARX(p, q, n) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

18.3346, 26.5990, 35.8624, 44.8289, 53.7512, 60.5086 ARX(1, 1, 2) 15.5184, 20.9192, 27.8176, 35.3589, 43.9210, 50.5047 ARX(2, 2, 2) 15.3592, 20.1736, 25.4967, 32.6258, 40.4916, 47.2481 ARX(3, 2, 2) 15.3224, 19.8423, 25.0511, 31.9170, 38.9812, 45.6829 ARX(4, 2, 2) 15.3200, 19.8433, 25.0510, 31.9146, 38.9858, 45.6849 ARX(5, 2, 2) 15.4286, 19.9065, 25.7220, 32.6493, 39.6406, 46.0115 ARX(6, 2, 2) 15.3576, 19.9718, 25.4068, 32.3474, 39.0521, 45.5616 ARX(7, 2, 2) 15.4913, 19.5610, 25.4153, 32.2240, 39.3885, 45.8530 ARX(8, 2, 2) 15.3410, 19.6328, 25.6180, 32.6323, 39.8298, 46.6052 ARX(9, 2, 2) 15.4446, 19.6831, 25.6035, 32.8968, 40.6220, 47.7878 ARX(10, 2, 2)

runMain scalation.modeling.forecasting.example_CovidTest15

Attributes

def example_CovidTest16(): Unit

The example_CovidTest16 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Exogenous ARX(p, q, n) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest16 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Exogenous ARX(p, q, n) models for several p values, and horizons 1 to 6, see sMAPE metrics below:

12.2356, 20.6830, 35.2603, 43.9974, 51.5944, 52.0301 ARX(1, 1, 2) 9.72391, 20.6254, 25.4950, 34.2458, 44.5078, 49.9804 ARX(2, 2, 2) 10.0738, 21.4470, 26.2178, 34.2212, 44.0982, 49.4524 ARX(3, 2, 2) 9.29391, 19.6487, 22.8980, 31.6528, 41.6049, 46.9430 ARX(4, 2, 2) 10.2806, 19.2649, 23.1211, 32.1942, 41.9189, 47.2119 ARX(5, 2, 2) 11.4258, 19.7370, 24.5103, 34.4673, 44.9873, 49.7458 ARX(6, 2, 2) 11.2501, 19.0128, 22.3547, 31.9938, 42.1729, 47.1063 ARX(7, 2, 2) 10.9763, 18.8067, 22.5181, 32.0960, 41.8394, 47.1825 ARX(8, 2, 2) 11.1796, 19.3087, 23.7479, 33.1067, 42.8283, 47.6904 ARX(9, 2, 2) 10.9499, 20.6255, 25.8116, 35.5139, 45.2163, 50.0280 ARX(10, 2, 2)

runMain scalation.modeling.forecasting.example_CovidTest16

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def example_CovidTest2(): Unit

The example_CovidTest2 main function tests the Example_Covid object. Performs Exploratory Data Analysis (EDA) to find relationships between contemporaneous variables.

The example_CovidTest2 main function tests the Example_Covid object. Performs Exploratory Data Analysis (EDA) to find relationships between contemporaneous variables.

runMain scalation.modeling.forecasting.example_CovidTest2

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def example_CovidTest3(): Unit

The example_CovidTest3 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs several baseline models for horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest3 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs several baseline models for horizons 1 to 6, see sMAPE metrics below:

55.1927, 53.9282, 52.7133, 51.8648, 51.9621, 52.0771 Null 54.6045, 53.3254, 52.1120, 51.2903, 51.4475, 51.4937 Trend 24.2641, 31.8588, 42.4430, 50.1029, 57.4933, 63.5406 SMA 26.4055, 31.5936, 43.7356, 50.1744, 58.3506, 63.7234 WMA 18.6934, 29.1811, 38.6542, 47.1281, 54.8713, 61.9944 SES 19.0371, 29.5797, 39.0740, 47.4638, 55.1785, 62.1818 RW 18.3265, 28.7734, 38.2039, 46.7814, 54.5563, 61.7930 RWS 18.7298, 28.4908, 37.0997, 45.6487, 51.7248, 56.3708 AR(1)

runMain scalation.modeling.forecasting.example_CovidTest3

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def example_CovidTest4(): Unit

The example_CovidTest4 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs several baseline models for horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest4 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs several baseline models for horizons 1 to 6, see sMAPE metrics below:

57.1057, 60.0825, 62.9136, 64.7453, 67.9247, 70.6674 Null 61.9077, 65.1881, 68.7187, 71.4655, 73.9327, 75.9584 Trend 22.3044, 30.4325, 45.3661, 55.7217, 67.6973, 77.4038 SMA 23.8526, 30.0945, 46.9748, 55.8104, 68.7352, 77.7010 WMA 18.3769, 27.1712, 40.3425, 51.8124, 63.7356, 75.0046 SES 18.6713, 27.5720, 40.9387, 52.3496, 64.2481, 75.3015 RW 18.0855, 26.7084, 39.6941, 51.2218, 63.1873, 74.6834 RWS 19.1590, 31.1975, 44.4850, 55.3120, 65.5536, 74.4969 AR(1)

runMain scalation.modeling.forecasting.example_CovidTest4

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def example_CovidTest5(): Unit

The example_CovidTest5 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive AR(p) models for several p values and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest5 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive AR(p) models for several p values and horizons 1 to 6, see sMAPE metrics below:

18.7298, 28.4908, 37.0997, 45.6487, 51.7248, 56.3708 AR(1) 16.3579, 24.7155, 33.0480, 40.0707, 46.0049, 50.8265 AR(2) 16.0114, 22.7408, 29.5631, 35.2773, 40.9870, 45.8408 AR(3) 15.8988, 22.5738, 28.5298, 33.3360, 39.1586, 43.1606 AR(4) 15.9279, 22.5769, 28.5035, 33.3019, 39.1381, 43.0520 AR(5) 15.9647, 22.6143, 28.5229, 33.3735, 39.1651, 42.9640 AR(6) 16.0207, 23.2172, 29.4751, 35.2827, 41.0976, 46.1932 AR(7) 16.0501, 22.7281, 28.6740, 34.1866, 39.5963, 44.9223 AR(8) 16.0196, 22.5269, 28.4223, 34.1619, 39.7297, 44.4649 AR(9) 16.1069, 22.6213, 28.6435, 34.2722, 39.9638, 44.8023 AR(10)

runMain scalation.modeling.forecasting.example_CovidTest5

Attributes

def example_CovidTest6(): Unit

The example_CovidTest6 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive AR(p) models for several p values and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest6 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive AR(p) models for several p values and horizons 1 to 6, see sMAPE metrics below:

19.1590, 31.1975, 44.4850, 55.3120, 65.5536, 74.4969 AR(1) 17.1764, 27.8131, 41.0173, 52.3883, 62.4018, 71.3206 AR(2) 16.1569, 24.1092, 35.0634, 45.3502, 56.0450, 65.4998 AR(3) 15.2413, 23.2293, 30.1320, 40.3648, 48.8558, 57.8766 AR(4) 15.4399, 23.3058, 30.4161, 40.4655, 49.3913, 58.6573 AR(5) 15.7443, 22.8374, 29.7678, 38.5566, 45.5084, 50.8096 AR(6) 15.8906, 24.2516, 31.1198, 40.2877, 47.4982, 56.6783 AR(7) 15.8394, 24.8442, 31.2414, 40.4416, 47.5974, 56.3880 AR(8) 15.2112, 23.6265, 30.7560, 40.1489, 49.4426, 58.3781 AR(9) 15.7954, 23.7332, 32.8467, 42.5300, 52.3179, 60.5518 AR(10)

runMain scalation.modeling.forecasting.example_CovidTest6

Attributes

def example_CovidTest7(): Unit

The example_CovidTest7 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Moving Average ARMA(p, 0) models for several p and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest7 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Moving Average ARMA(p, 0) models for several p and horizons 1 to 6, see sMAPE metrics below:

20.2191, 29.9108, 38.1525, 45.5858, 52.2918, 57.3670 ARMA(1, 0) 17.7900, 25.3293, 33.3283, 39.5055, 44.9095, 50.6043 ARMA(2, 0) 17.4057, 23.9135, 30.5357, 35.5950, 40.6434, 46.4122 ARMA(3, 0) 17.2928, 23.6678, 29.5574, 34.0383, 38.9062, 44.1568 ARMA(4, 0) 17.2850, 23.6708, 29.5699, 34.0520, 38.9330, 44.2125 ARMA(5, 0) 17.3271, 23.9829, 29.9874, 34.6032, 39.0682, 43.6979 ARMA(6, 0) 17.2335, 24.0097, 29.9465, 34.3426, 38.9182, 44.4357 ARMA(7, 0) 17.2811, 23.7288, 29.5992, 34.0946, 38.6983, 44.1365 ARMA(8, 0) 17.2044, 23.6396, 29.5609, 34.2834, 38.9406, 44.1984 ARMA(9, 0) 17.2588, 23.6012, 29.4737, 34.3447, 39.0981, 44.1297 ARMA(10, 0)

runMain scalation.modeling.forecasting.example_CovidTest7

Attributes

def example_CovidTest8(): Unit

The example_CovidTest8 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Moving Average ARMA(p, 0) models for several p values and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest8 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Moving Average ARMA(p, 0) models for several p values and horizons 1 to 6, see sMAPE metrics below:

19.0003, 30.3936, 43.8008, 54.8254, 65.3736, 74.5465 ARMA(1, 0) 17.0385, 26.7633, 39.4985, 51.0132, 61.2488, 70.4454 ARMA(2, 0) 16.0454, 22.1844, 31.7033, 41.1297, 51.6017, 61.3707 ARMA(3, 0) 15.2966, 20.7829, 27.7076, 36.3322, 41.5452, 49.0153 ARMA(4, 0) 15.6244, 20.6003, 29.0435, 36.8354, 43.1722, 48.1613 ARMA(5, 0) 15.6619, 23.1335, 32.0946, 41.3166, 50.0557, 60.0608 ARMA(6, 0) 16.0957, 22.2142, 32.4196, 39.8389, 47.6075, 51.5675 ARMA(7, 0) 15.8659, 25.6319, 36.0707, 45.6189, 54.9417, 58.8670 ARMA(8, 0) 15.5716, 24.2525, 34.1386, 44.2350, 55.1113, 60.8057 ARMA(9, 0) 14.9008, 22.6571, 30.4335, 41.6601, 50.1669, 61.2246 ARMA(10, 0)

runMain scalation.modeling.forecasting.example_CovidTest8

Attributes

def example_CovidTest9(): Unit

The example_CovidTest9 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Lagged Regression ARY(p) models for several p values and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest9 main function tests the Example_Covid object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive, Lagged Regression ARY(p) models for several p values and horizons 1 to 6, see sMAPE metrics below:

20.1794, 29.8589, 38.1450, 45.5634, 52.3478, 57.4474 ARY(1) 17.7728, 25.1705, 33.1900, 39.4218, 44.8621, 50.5991 ARY(2) 17.3594, 23.7550, 30.3838, 35.4514, 40.5868, 46.4292 ARY(3) 17.2457, 23.5122, 29.4110, 33.9350, 38.8422, 44.2303 ARY(4) 17.2314, 23.5178, 29.4345, 33.9602, 38.9022, 44.3249 ARY(5) 17.2503, 23.8232, 29.8341, 34.4885, 39.0138, 43.8011 ARY(6) 17.1625, 23.8385, 29.8227, 34.2751, 38.9853, 44.6092 ARY(7) 17.2067, 23.5579, 29.4741, 34.0077, 38.6431, 44.3218 ARY(8) 17.1326, 23.4530, 29.4149, 34.1103, 38.8254, 44.3564 ARY(9) 17.1791, 23.4175, 29.3213, 34.1509, 38.8917, 44.2659 ARY(10)

runMain scalation.modeling.forecasting.example_CovidTest9

Attributes

def example_GasFurnaceTest(): Unit

The example_GasFurnaceTest main function test the Example_GasFurnace object.

The example_GasFurnaceTest main function test the Example_GasFurnace object.

runMain scalation.modeling.forecasting.example_GasFurnaceTest

Attributes

def example_GasFurnaceTest2(): Unit

The example_GasFurnaceTest2 main function test the Example_GasFurnace object. This performs Exploratory Data Analysis (EDA) to find relationships between contemporaneous variables.

The example_GasFurnaceTest2 main function test the Example_GasFurnace object. This performs Exploratory Data Analysis (EDA) to find relationships between contemporaneous variables.

runMain scalation.modeling.forecasting.example_GasFurnaceTest2

Attributes

def example_ILITest(): Unit

The example_ILITest main function test the Example_ILI object. Plots the response column.

The example_ILITest main function test the Example_ILI object. Plots the response column.

runMain scalation.modeling.forecasting.example_ILITest

Attributes

def example_ILITest10(): Unit

The example_ILITest10 main function test the Example_ILI object. This test compares the ARX_Symb and ARX_Symb_D models for several values of p and q.

The example_ILITest10 main function test the Example_ILI object. This test compares the ARX_Symb and ARX_Symb_D models for several values of p and q.

runMain scalation.modeling.forecasting.example_ILITest10

Attributes

def example_ILITest2(): Unit

The example_ILITest2 main function test the Example_ILI object. This performs Exploratory Data Analysis (EDA) to find relationships between contemporaneous variables.

The example_ILITest2 main function test the Example_ILI object. This performs Exploratory Data Analysis (EDA) to find relationships between contemporaneous variables.

runMain scalation.modeling.forecasting.example_ILITest2

Attributes

def example_ILITest3(): Unit

The example_ILITest3 main function tests the Example_ILI object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs several baseline models for horizons 1 to 6, see sMAPE metrics below:

The example_ILITest3 main function tests the Example_ILI object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs several baseline models for horizons 1 to 6, see sMAPE metrics below:

72.3771, 72.3020, 72.2361, 72.1766, 72.1235, 72.0677 Null 63.3460, 63.3241, 63.3049, 63.2781, 63.2447, 63.2163 Trend 14.8647, 21.3947, 30.4372, 37.5090, 44.7307, 51.0374 SMA 16.3572, 21.1826, 31.5004, 37.5687, 45.4430, 51.2477 WMA 10.7057, 19.2054, 27.4321, 35.0421, 42.2938, 48.9523 SES 10.9952, 19.5427, 27.7590, 35.3626, 42.5756, 49.2132 RW 10.4080, 18.8430, 27.0731, 34.6972, 41.9847, 48.6709 RWS 13.0155, 22.9537, 31.6664, 39.0945, 45.5909, 51.2390 AR(1)

runMain scalation.modeling.forecasting.example_ILITest3

Attributes

def example_ILITest5(): Unit

The example_CovidTest5 main function tests the Example_ILI object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive AR(p) models for several p values and horizons 1 to 6, see sMAPE metrics below:

The example_CovidTest5 main function tests the Example_ILI object. Uses In-Sample Testing (In-ST), i.e., train and test on the same data. Runs Auto-Regressive AR(p) models for several p values and horizons 1 to 6, see sMAPE metrics below:

13.0155, 22.9537, 31.6664, 39.0945, 45.5909, 51.2390 AR(1) 12.2138, 22.3784, 31.4101, 38.9114, 45.5691, 50.9743 AR(2) 12.1428, 22.6277, 32.1664, 39.8123, 46.2555, 51.4107 AR(3) 12.1593, 22.6250, 32.1326, 39.7545, 46.1988, 51.3480 AR(4) 12.2947, 22.7086, 31.6933, 38.7545, 44.9812, 50.1115 AR(5) 13.1465, 23.9406, 33.3826, 40.4398, 46.4482, 51.2484 AR(6) 13.1506, 23.9510, 33.3948, 40.4620, 46.4715, 51.2688 AR(7) 12.8000, 23.7886, 33.2327, 40.0353, 45.6288, 50.3420 AR(8) 12.8755, 23.7681, 33.0763, 39.8769, 45.5340, 50.3367 AR(9) 12.8060, 23.7307, 33.2322, 40.0946, 45.7090, 50.5064 AR(10)

runMain scalation.modeling.forecasting.example_ILITest5

Attributes

def example_ILITest6(): Unit

The example_ILITest6 main function test the Example_ILI object. This test compares the ARMA model for several values of p and q.

The example_ILITest6 main function test the Example_ILI object. This test compares the ARMA model for several values of p and q.

runMain scalation.modeling.forecasting.example_ILITest6

Attributes

def example_ILITest7(): Unit

The example_ILITest7 main function test the Example_ILI object. This test compares the ARY model for several values of p.

The example_ILITest7 main function test the Example_ILI object. This test compares the ARY model for several values of p.

runMain scalation.modeling.forecasting.example_ILITest7

Attributes

def example_ILITest8(): Unit

The example_ILITest8 main function test the Example_ILI object. This test compares the ARY_D model for several values of p.

The example_ILITest8 main function test the Example_ILI object. This test compares the ARY_D model for several values of p.

runMain scalation.modeling.forecasting.example_ILITest8

Attributes

def example_ILITest9(): Unit

The example_ILITest9 main function test the Example_ILI object. This test compares the ARIMA model for several values of p and q.

The example_ILITest9 main function test the Example_ILI object. This test compares the ARIMA model for several values of p and q.

runMain scalation.modeling.forecasting.example_ILITest9

Attributes

def forecastMatrixTest(): Unit

The forecastMatrixTest main function tests the RandomWalk class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The forecastMatrixTest main function tests the RandomWalk class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.forecastMatrixTest

def forecastMatrixTest2(): Unit

The forecastMatrixTest2 main function tests the RandomWalk class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The forecastMatrixTest2 main function tests the RandomWalk class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.forecastMatrixTest2

def forecastMatrixTest3(): Unit

The forecastMatrixTest3 main function tests the RandomWalk class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The forecastMatrixTest3 main function tests the RandomWalk 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.forecastMatrixTest3

Attributes

def forecastMatrixTest4(): Unit

The forecastMatrixTest4 main function tests the RandomWalk class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The forecastMatrixTest4 main function tests the RandomWalk class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.forecastMatrixTest4

Attributes

def makeTSeries(signal: FunctionS2S, m: Int, noise: Variate): VectorD

The makeTSeries top level function generates time-series data.

The makeTSeries top level function generates time-series data.

Value parameters

m

the length of the time series

noise

the random variate generator used for the noise part

signal

the function of time used to make the deterministic part

Attributes

def makeTSeriesR(c: Double, φ: VectorD, m: Int, noise: Variate): VectorD

The makeTSeries top level function recursively generates time-series data by simulating and AR process. y_t+1 = δ + Σ(φ_j y_t-j) + e_t+1 Note: all defaults generates white noise with variance 1

The makeTSeries top level function recursively generates time-series data by simulating and AR process. y_t+1 = δ + Σ(φ_j y_t-j) + e_t+1 Note: all defaults generates white noise with variance 1

Value parameters

c

the initial value for the time series

m

the length of the time series

noise

the random variate generator used for the noise part

φ

the auto-regressive coefficients

Attributes

def nullModelTest(): Unit

The nullModelTest main function tests the NullModel class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The nullModelTest main function tests the NullModel class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.nullModelTest

def nullModelTest2(): Unit

The nullModelTest2 main function tests the NullModel class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The nullModelTest2 main function tests the NullModel class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.nullModelTest2

def nullModelTest3(): Unit

The nullModelTest3 main function tests the NullModel class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The nullModelTest3 main function tests the NullModel 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.nullModelTest3

Attributes

def nullModelTest4(): Unit

The nullModelTest4 main function tests the NullModel class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The nullModelTest4 main function tests the NullModel class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.nullModelTest4

Attributes

def nullModelTest5(): Unit

The nullModelTest5 main function tests the NullModel class on small dataset. Test forecasts (h = 1 step ahead forecasts).

The nullModelTest5 main function tests the NullModel class on small dataset. Test forecasts (h = 1 step ahead forecasts).

runMain scalation.modeling.forecasting.nullModelTest5

Attributes

def periodogramTest(): Unit

The periodogramTest main function tests the Periodogram class on real data.

The periodogramTest main function tests the Periodogram class on real data.

runMain scalation.modeling.forecasting.periodogramTest

Attributes

def randomWalkSTest(): Unit

The randomWalkSTest main function tests the RandomWalkS class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkSTest main function tests the RandomWalkS class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.randomWalkSTest

def randomWalkSTest2(): Unit

The randomWalkSTest2 main function tests the RandomWalkS class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkSTest2 main function tests the RandomWalkS class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.randomWalkSTest2

def randomWalkSTest3(): Unit

The randomWalkSTest3 main function tests the RandomWalkS class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkSTest3 main function tests the RandomWalkS 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.randomWalkSTest3

Attributes

def randomWalkSTest4(): Unit

The randomWalkSTest4 main function tests the RandomWalkS class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkSTest4 main function tests the RandomWalkS class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.randomWalkSTest4

Attributes

def randomWalkTest(): Unit

The randomWalkTest main function tests the RandomWalk class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkTest main function tests the RandomWalk class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.randomWalkTest

def randomWalkTest2(): Unit

The randomWalkTest2 main function tests the RandomWalk class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkTest2 main function tests the RandomWalk class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.randomWalkTest2

def randomWalkTest3(): Unit

The randomWalkTest3 main function tests the RandomWalk class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkTest3 main function tests the RandomWalk 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.randomWalkTest3

Attributes

def randomWalkTest4(): Unit

The randomWalkTest4 main function tests the RandomWalk class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkTest4 main function tests the RandomWalk class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.randomWalkTest4

Attributes

def randomWalkTest5(): Unit

The randomWalkTest5 main function tests the RandomWalk class on small dataset. Test forecasts (h = 1 step ahead forecasts).

The randomWalkTest5 main function tests the RandomWalk class on small dataset. Test forecasts (h = 1 step ahead forecasts).

runMain scalation.modeling.forecasting.randomWalkTest5

Attributes

def randomWalkTest6(stm: Int): Unit

The randomWalkTest6 main function tests the RandomWalk class on small dataset. Test forecasts (h = 1 step ahead forecasts).

The randomWalkTest6 main function tests the RandomWalk class on small dataset. Test forecasts (h = 1 step ahead forecasts).

runMain scalation.modeling.forecasting.randomWalkTest6

Value parameters

stm

the random number stream to use (command-line argument, e.g., 2)

Attributes

def sARYTest(): Unit

The RYTest main function tests the SARY class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The RYTest main function tests the SARY class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.sARYTest

def sARYTest2(): Unit

The sARYTest2 main function tests the SARY class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The sARYTest2 main function tests the SARY class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.sARYTest2

def sARYTest3(): Unit

The sARYTest3 main function tests the SARY class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The sARYTest3 main function tests the SARY 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.sARYTest3

Attributes

def sARYTest4(): Unit

The sARYTest4 main function tests the SARY class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The sARYTest4 main function tests the SARY class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.sARYTest4

Attributes

def sARYTest5(): Unit

The sARYTest5 main function tests the SARY class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

The sARYTest5 main function tests the SARY class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts). This version performs feature selection.

runMain scalation.modeling.forecasting.sARYTest5

Attributes

def simpleExpSmoothingTest(): Unit

The simpleExpSmoothingTest main function tests the SimpleExpSmoothing class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleExpSmoothingTest main function tests the SimpleExpSmoothing class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.simpleExpSmoothingTest

def simpleExpSmoothingTest2(): Unit

The simpleExpSmoothingTest2 main function tests the SimpleExpSmoothing class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleExpSmoothingTest2 main function tests the SimpleExpSmoothing class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.simpleExpSmoothingTest2

def simpleExpSmoothingTest3(): Unit

The simpleExpSmoothingTest3 main function tests the SimpleExpSmoothing class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleExpSmoothingTest3 main function tests the SimpleExpSmoothing 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.simpleExpSmoothingTest3

Attributes

def simpleExpSmoothingTest4(): Unit

The simpleExpSmoothingTest4 main function tests the SimpleExpSmoothing class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleExpSmoothingTest4 main function tests the SimpleExpSmoothing class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.simpleExpSmoothingTest4

Attributes

def simpleMovingAverageTest(): Unit

The simpleMovingAverageTest main function tests the SimpleMovingAverage class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleMovingAverageTest main function tests the SimpleMovingAverage class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.simpleMovingAverageTest

def simpleMovingAverageTest2(): Unit

The simpleMovingAverageTest2 main function tests the SimpleMovingAverage class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleMovingAverageTest2 main function tests the SimpleMovingAverage class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.simpleMovingAverageTest2

def simpleMovingAverageTest3(): Unit

The simpleMovingAverageTest3 main function tests the SimpleMovingAverage class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleMovingAverageTest3 main function tests the SimpleMovingAverage 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.simpleMovingAverageTest3

Attributes

def simpleMovingAverageTest4(): Unit

The simpleMovingAverageTest4 main function tests the SimpleMovingAverage class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleMovingAverageTest4 main function tests the SimpleMovingAverage class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.simpleMovingAverageTest4

Attributes

def stationarity_KPSSTest(): Unit

The stationarity_KPSSTest main function is used to test the Stationarity_KPSS class. Test whether white noise time-series has a unit root.

The stationarity_KPSSTest main function is used to test the Stationarity_KPSS class. Test whether white noise time-series has a unit root.

runMain scalation.modeling.forecasting.stationarity_KPSSTest

Attributes

def stationarity_KPSSTest2(): Unit

The stationarity_KPSSTest2 main function is used to test the Stationarity_KPSS class. Test whether the Lake Levels time-series has a unit root.

The stationarity_KPSSTest2 main function is used to test the Stationarity_KPSS class. Test whether the Lake Levels time-series has a unit root.

runMain scalation.modeling.forecasting.stationarity_KPSSTest2

Attributes

def stationarity_KPSSTest3(): Unit

The stationarity_KPSSTest3 main function is used to test the Stationarity_KPSS class. Test whether the differenced Lake Levels time-series has a unit root.

The stationarity_KPSSTest3 main function is used to test the Stationarity_KPSS class. Test whether the differenced Lake Levels time-series has a unit root.

runMain scalation.modeling.forecasting.stationarity_KPSSTest3

Attributes

def stationaryTest(): Unit

The stationaryTest main function tests the Stationary class on a simulated time-series.

The stationaryTest main function tests the Stationary class on a simulated time-series.

runMain scalation.modeling.forecasting.stationaryTest

Attributes

def stationaryTest2(): Unit

The stationaryTest2 main function tests the Stationary class on a simulated stationary time-series. An AR(1) is a stationary process when |φ_1| < 1, a unit root process when |φ_1| = 1, and explosive otherwise.

The stationaryTest2 main function tests the Stationary class on a simulated stationary time-series. An AR(1) is a stationary process when |φ_1| < 1, a unit root process when |φ_1| = 1, and explosive otherwise.

runMain scalation.modeling.forecasting.stationaryTest2

Attributes

def stationaryTest3(): Unit

The stationaryTest3 main function tests the Stationary class on a simulated stationary time-series. An AR(2) is a stationary process when |φ_2| < 1 and |φ_1| < 1 - φ_2, a unit root process when |φ_2| = 1 or |φ_1| = 1 - φ_2, and explosive otherwise.

The stationaryTest3 main function tests the Stationary class on a simulated stationary time-series. An AR(2) is a stationary process when |φ_2| < 1 and |φ_1| < 1 - φ_2, a unit root process when |φ_2| = 1 or |φ_1| = 1 - φ_2, and explosive otherwise.

runMain scalation.modeling.forecasting.stationaryTest3

Attributes

def tranARYTest(): Unit

The tranARYTest main function tests the TranARY class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The tranARYTest main function tests the TranARY class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.traARYTest

def tranARYTest2(): Unit

The tranARYTest2 main function tests the TranARY class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The tranARYTest2 main function tests the TranARY class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.tranARYTest2

def tranARYTest3(): Unit

The tranARYTest3 main function tests the TranARY class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The tranARYTest3 main function tests the TranARY 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.tranARYTest3

Attributes

def tranARYTest4(): Unit

The tranARYTest4 main function tests the TranARY class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The tranARYTest4 main function tests the TranARY class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.tranARYTest4

Attributes

def trendModelTest(): Unit

The trendModelTest main function tests the TrendModel class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The trendModelTest main function tests the TrendModel class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.trendModelTest

def trendModelTest2(): Unit

The trendModelTest2 main function tests the TrendModel class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The trendModelTest2 main function tests the TrendModel class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.trendModelTest2

def trendModelTest3(): Unit

The trendModelTest3 main function tests the TrendModel class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The trendModelTest3 main function tests the TrendModel 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.trendModelTest3

Attributes

def trendModelTest4(): Unit

The trendModelTest4 main function tests the TrendModel class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The trendModelTest4 main function tests the TrendModel class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.trendModelTest4

Attributes

def weightedMovingAverageTest(): Unit

The weightedMovingAverageTest main function tests the WeightedMovingAverage class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The weightedMovingAverageTest main function tests the WeightedMovingAverage class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.weightedMovingAverageTest

def weightedMovingAverageTest2(): Unit

The weightedMovingAverageTest2 main function tests the WeightedMovingAverage class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The weightedMovingAverageTest2 main function tests the WeightedMovingAverage class on real data: Forecasting Lake Levels using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

Attributes

See also

cran.r-project.org/web/packages/fpp/fpp.pdf

runMain scalation.modeling.forecasting.weightedMovingAverageTest2

def weightedMovingAverageTest3(): Unit

The weightedMovingAverageTest3 main function tests the WeightedMovingAverage class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The weightedMovingAverageTest3 main function tests the WeightedMovingAverage 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.weightedMovingAverageTest3

Attributes

def weightedMovingAverageTest4(): Unit

The weightedMovingAverageTest4 main function tests the WeightedMovingAverage class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

The weightedMovingAverageTest4 main function tests the WeightedMovingAverage class on real data: Forecasting COVID-19 using Train-n-Test Split (TnT) with Rolling Validation. Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.weightedMovingAverageTest4

Attributes

Extensions

Extensions

extension (tr: Transform | Array[Transform])
def apply(i: Int): Transform
def f(x: VectorD): VectorD
def f(x: MatrixD): MatrixD
def fi(x: VectorD): VectorD
def fi(x: MatrixD): MatrixD
def length: Int
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