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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
package scalation.modeling.forecasting.nonlinear
package scalation.modeling.forecasting.reg_trees

Type members

Classlikes

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

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 NoSubModels
trait Correlogram
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
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
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 Forecaster

The ARMA class fits an Auto-Regressive Moving Average model using a state-space representation and Kalman-filter maximum likelihood.

The ARMA class fits an Auto-Regressive Moving Average model using a state-space representation and Kalman-filter maximum likelihood.

Model: y_t = c + Σ_j φ_j y_{t-j} + Σ_k θ_k e_{t-k} + e_t

The implementation estimates:

  • AR coefficients φ
  • MA coefficients θ
  • intercept c
  • process variance σ² Notes:
  • The likelihood is computed from one-step-ahead Kalman innovations.
  • A small burn-in (max(p, q+1)) is excluded from the log-likelihood sum to align with the external reference implementation.
  • Rolling forecast evaluation is performed externally in the test driver.

Value parameters

bakcast

whether a backcast value is prepended

hh

the maximum forecast horizon

hparam

the hyper-parameters

tRng

the optional time range

y

the response/time-series vector

Attributes

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

The ARMA companion object provides factory methods and default hyperparameters for ARMA models.

The ARMA companion object provides factory methods and default hyperparameters for ARMA models.

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 Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARX_Quad
class ARX_SR
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.

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.

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 Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class NARX_SR_D
class ARX_Quad_D
class ARX_SR_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 Forecast
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 Forecast
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
object ARX_SR extends MakeMatrix4TS

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

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

Attributes

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

The ARX_SR 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_SR 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)

fExo_sz

the array of the number of transformations for each exogenous

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_SR.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 Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object ARX_SR_D extends MakeMatrix4TS

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX_SR_D.type
class ARX_SR_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_SR_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_SR_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_SR_D.apply

y

the response/output vector (main time series data)

Attributes

See also

MakeMatrix4TS for hyper-parameter specifications.

Companion
object
Supertypes
class ARX_D
class Forecaster_D
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object 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(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 Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARY_Quad
class SARY
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 Forecast
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 Forecast
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(dfr: Double, df: Double) extends Fit

The Diagnoser trait provides methods to determine basic Quality of Fit QoF measures for time series where forecasting at early time points may not be feasible.

The Diagnoser trait provides methods to determine basic Quality of Fit QoF measures for time series where forecasting at early time points may not be feasible.

Value parameters

df

the degrees of freedom for error

dfr

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

Attributes

Supertypes
trait Fit
trait FitM
class Object
trait Matchable
class Any
Known subtypes
class AR_Star
class VAR
class VARX
class Forecaster
class AR
class ARMA
class Forecaster_D
class ARX_D
class NARX_SR_D
class ARX_Quad_D
class ARX_SR_D
class ARY_D
class ARX
class ARX_Quad
class ARX_SR
class ARY
class ARY_Quad
class SARY
class NullModel
class RandomWalk
class RandomWalkS
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-Sample, 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 Cholesky Factorization (f: 11, 12) ARY_D(p) Models CSSE + Direct QR, Cholesky Factorization (g: 13, 14) ARMA(p, q=1) Models CSSE BFGS? Multivariate: (h: 15, 16) ARX(p, 2, 2) Models CSSE Cholesky Factorization (i: 17, 18) ARX_D Models CSSE + Direct QR, Cholesky Factorization (j: 19, 20) ARX_Quad(p, 2, 2) Models CSSE Cholesky Factorization (k: 21, 22) ARX_Quad_D Models CSSE + Direct QR, Cholesky Factorization

The Example_Covid object provides a convenient way to load Covid-19 weekly data. See test cases (odd In-Sample, 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 Cholesky Factorization (f: 11, 12) ARY_D(p) Models CSSE + Direct QR, Cholesky Factorization (g: 13, 14) ARMA(p, q=1) Models CSSE BFGS? Multivariate: (h: 15, 16) ARX(p, 2, 2) Models CSSE Cholesky Factorization (i: 17, 18) ARX_D Models CSSE + Direct QR, Cholesky Factorization (j: 19, 20) ARX_Quad(p, 2, 2) Models CSSE Cholesky Factorization (k: 21, 22) ARX_Quad_D Models CSSE + Direct QR, Cholesky Factorization

Known Bugs: SMA, WMA, SES, ARMA, ARY_D, ARX_D, ARX_Quad_D

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 Filter(y: VectorD)

The Filter trait provides basic time series capabilities for filters. A filter is used to pull out the important information from a time series. Commonly, this involves improving the signal-to-noise ratio, which is often accomplished by using a low-pass filter that remove high frequencies. Such filters are also called smoothers (the smoothed time series has less abrupt changes)

The Filter trait provides basic time series capabilities for filters. A filter is used to pull out the important information from a time series. Commonly, this involves improving the signal-to-noise ratio, which is often accomplished by using a low-pass filter that remove high frequencies. Such filters are also called smoothers (the smoothed time series has less abrupt changes)

Value parameters

y

the response vector (time series data)

Attributes

Supertypes
class Object
trait Matchable
class Any
Known subtypes
class SGFilter
trait Forecast extends Model

The Forecast trait serves a minimal adpater of Model to the Forecast task. Most of implementation is in the Forecaster abstract class below.

The Forecast trait serves a minimal adpater of Model to the Forecast task. Most of implementation is in the Forecaster abstract class below.

Attributes

Supertypes
trait Model
class Object
trait Matchable
class Any
Known subtypes
class AR_Star
class Forecaster
class AR
class ARMA
class Forecaster_D
class ARX_D
class NARX_SR_D
class ARX_Quad_D
class ARX_SR_D
class ARY_D
class ARX
class ARX_Quad
class ARX_SR
class ARY
class ARY_Quad
class SARY
class NullModel
class RandomWalk
class RandomWalkS
class TrendModel
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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 Forecaster_D
class ARX_D
class NARX_SR_D
class ARX_Quad_D
class ARX_SR_D
class ARY_D
class ARX
class ARX_Quad
class ARX_SR
class ARY
class ARY_Quad
class SARY
class NullModel
class RandomWalk
class RandomWalkS
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(y: VectorD, hh: Int, tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends Diagnoser, ForecastMatrix, Forecast

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 Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class AR
class ARMA
class Forecaster_D
class ARX_D
class NARX_SR_D
class ARX_Quad_D
class ARX_SR_D
class ARY_D
class ARX
class ARX_Quad
class ARX_SR
class ARY
class ARY_Quad
class SARY
class NullModel
class RandomWalk
class RandomWalkS
class TrendModel
Show all
abstract class Forecaster_D(x: MatrixD, y: MatrixD, hh: Int, fname: Array[String], tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends Forecaster

The Forecaster_D abstract class provides a common framework for several forecasters.

The Forecaster_D abstract class provides a common framework for several forecasters.

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 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

Note

Forecaster_D is dependent on Forecaster_Reg class to do feature selection. Note, the train_x method must be called first followed by test.

Supertypes
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
class ARX_D
class NARX_SR_D
class ARX_Quad_D
class ARX_SR_D
class ARY_D
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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 regularized regression for time series forecasting.

The Forecaster_Reg abstract class provides base methods for use by extending classes that utilize regularized 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 for regularization, the data will be centered (so NO INTERCEPT COLUMN)

y

the response/output vector (time series data)

Attributes

Supertypes
class Forecaster
trait Forecast
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_SR
class ARY
class ARY_Quad
class SARY
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class KalmanFilter(val f: MatrixD, val q: MatrixD, val h: MatrixD, val r: MatrixD, var x: VectorD, var p: MatrixD)

The KalmanFilter class provides a simple implementation of a Kalman filter. It is useful for smoothing noisy data and for providing better estimates of the state of a system.

The KalmanFilter class provides a simple implementation of a Kalman filter. It is useful for smoothing noisy data and for providing better estimates of the state of a system.

Value parameters

f

the state transition matrix

h

the measurement matrix

p

the initial covariance matrix

q

the process noise covariance matrix

r

the measurement noise covariance matrix

x

the initial state vector

Attributes

Supertypes
class Object
trait Matchable
class Any
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_SR
object ARX_SR_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, NoSubModels

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
trait NoSubModels
class Forecaster
trait Forecast
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
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 RandomWalk(y: VectorD, hh: Int, tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends Forecaster, NoSubModels

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
trait NoSubModels
class Forecaster
trait Forecast
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 RandomWalkS(y: VectorD, hh: Int, tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends Forecaster, NoSubModels

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
trait NoSubModels
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
object SARIMA

The SARIMA companion object provides hyper-parameters for AR, ..., SARIMA and SARIMAX models. It also includes methods for seasonal differencing.

The SARIMA companion object provides hyper-parameters for AR, ..., SARIMA and SARIMAX models. It also includes methods for seasonal differencing.

Attributes

See also
Supertypes
class Object
trait Matchable
class Any
Self type
SARIMA.type
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 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. These models extend ARY models by including seasonal (periodic) lags. 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 the last p lagged values of y and the last ps seasonally lagged values.

The SARY class provides basic time series analysis capabilities for SARY models. These models extend ARY models by including seasonal (periodic) lags. 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 the last p lagged values of y and the last ps seasonally lagged values.

y_t = b dot x_t + e_t

where y_t is the value of y at time t, b is the parameter vector, x_t collects past lagged (both regular and seasonal) values, 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 Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
class SGFilter(y: VectorD) extends Filter

The SGFilter class provides basic time series capabilities for Savitzky–Golay filters that are used to smooth data. Note, would need to be adapted for use as a forecaster as it uses future data.

The SGFilter class provides basic time series capabilities for Savitzky–Golay filters that are used to smooth data. Note, would need to be adapted for use as a forecaster as it uses future data.

Value parameters

y

the response vector (time series data)

Attributes

See also

WeightedMovingAverage

Supertypes
trait Filter
class Object
trait Matchable
class Any
class SimpleExpSmoothing(y: VectorD, hh: Int, tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends Forecaster, Filter, NoSubModels

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
trait NoSubModels
trait Filter
class Forecaster
trait Forecast
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
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
class SimpleMovingAverage(y: VectorD, hh: Int, tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends Forecaster, NoSubModels

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
trait NoSubModels
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
Known subtypes
object SimpleMovingAverage2

The SimpleMovingAverage2 companion object provides factory methods for the SimpleMovingAverage2 class.

The SimpleMovingAverage2 companion object provides factory methods for the SimpleMovingAverage2 class.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
class SimpleMovingAverage2(y: VectorD, hh: Int, tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends Forecaster, NoSubModels

The SimpleMovingAverage2 class provides basic time series analysis capabilities for SimpleMovingAverage2 models. SimpleMovingAverage2 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 SimpleMovingAverage2 class provides basic time series analysis capabilities for SimpleMovingAverage2 models. SimpleMovingAverage2 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 SimpleMovingAverage2.hp)

tRng

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
trait NoSubModels
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
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
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
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
class TrendModel(y: VectorD, hh: Int, tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends Forecaster, NoSubModels

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
trait NoSubModels
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
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
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
class WeightedMovingAverage(y: VectorD, hh: Int, tRng: Range = ..., hparam: HyperParameter = ..., bakcast: Boolean = ...) extends SimpleMovingAverage

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
trait NoSubModels
class Forecaster
trait Forecast
trait Model
class Diagnoser
trait Fit
trait FitM
class Object
trait Matchable
class Any
Show all
final class aRIMA_diffTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRIMA_diffTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRIMA_diffTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRMA_KalmanRollingValidation

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 aRXTest6

Attributes

Supertypes
class Object
trait Matchable
class Any
final class aRXTest7

Attributes

Supertypes
class Object
trait Matchable
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final class aRX_DTest3

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final class aRX_DTest4

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final class aRX_DTest5

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final class aRX_QuadTest3

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final class aRX_QuadTest4

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final class aRX_QuadTest5

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final class aRX_QuadTest6

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final class aRX_QuadTest7

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final class aRX_Quad_DTest3

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final class aRX_Quad_DTest4

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final class aRX_Quad_DTest5

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final class aRX_SRTest3

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final class aRX_SRTest4

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final class aRX_SRTest5

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final class aRX_SRTest6

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final class aRX_SRTest7

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final class aRX_SR_DTest3

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final class aRX_SR_DTest4

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final class aRX_SR_DTest5

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final class aRYTest

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final class aRYTest2

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final class aRYTest3

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final class aRYTest4

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final class aRYTest5

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final class aRYTest6

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final class aRY_DTest

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final class aRY_DTest2

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final class aRY_DTest3

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final class aRY_QuadTest

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final class aRY_QuadTest2

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final class aRY_QuadTest3

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final class aRY_QuadTest4

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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_CovidTest13

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final class example_CovidTest14

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final class example_CovidTest15

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final class example_CovidTest16

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final class example_CovidTest17

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final class example_CovidTest18

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final class example_CovidTest19

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final class example_CovidTest2

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final class example_CovidTest20

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final class example_CovidTest21

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final class example_CovidTest22

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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_ILITest4

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final class example_ILITest5

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final class example_ILITest6

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final class example_ILITest7

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final class example_ILITest8

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class Object
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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 kalmanFilterTest

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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 randomWalkTest7

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final class reportBestTest

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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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class Object
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final class sGFilterTest

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final class sGFilterTest2

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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 simpleMovingAverage2Test

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final class simpleMovingAverage2Test2

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final class simpleMovingAverage2Test3

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final class simpleMovingAverage2Test4

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final class simpleMovingAverage2Test5

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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 simpleMovingAverageTest5

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class Object
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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 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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final class weightedMovingAverageTest5

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class Object
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 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 aRIMA_diffTest2(): Unit

The aRIMA_diffTest2 main function tests the ARIMA_diff object on real data: Forecasting Covid-19 new deaths comparing ARMA, AR1MA, Differenced ARMA, Transformed-Back Differenced ARMA. Observe that backform is better than undiff on predictions.

The aRIMA_diffTest2 main function tests the ARIMA_diff object on real data: Forecasting Covid-19 new deaths 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_diffTest2

def aRIMA_diffTest3(): Unit

The aRIMA_diffTest3 main function tests the ARIMA_diff object on real data: Forecasting Covid-19 new deaths examining phase space plots.

The aRIMA_diffTest3 main function tests the ARIMA_diff object on real data: Forecasting Covid-19 new deaths examining phase space plots.

runMain scalation.modeling.forecasting.aRIMA_diffTest3

Attributes

def aRMA_KalmanRollingValidation(): Unit

Rolling-origin validation driver for the standalone Kalman-filter ARMA implementation. The logic mirrors the external Python validation flow:

Rolling-origin validation driver for the standalone Kalman-filter ARMA implementation. The logic mirrors the external Python validation flow:

  • fit once on the training window
  • forecast horizons 1..hh before each newly revealed test observation
  • update the filter state sequentially without refitting

runMain scalation.modeling.forecasting.aRMA_KalmanRollingValidation

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 aRXTest6(): Unit

The aRXTest6 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). This version performs FEATURE SELECTION (training-set, training-set).

The aRXTest6 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). This version performs FEATURE SELECTION (training-set, training-set).

runMain scalation.modeling.forecasting.aRXTest6

Attributes

def aRXTest7(): Unit

The aRXTest7 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). This version performs FEATURE SELECTION (training-set, testing-set).

The aRXTest7 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). This version performs FEATURE SELECTION (training-set, testing-set).

runMain scalation.modeling.forecasting.aRXTest7

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_DTest5(): Unit

The aRX_DTest5 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). This version performs feature selection.

The aRX_DTest5 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). This version performs feature selection.

runMain scalation.modeling.forecasting.aRX_DTest5

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

Attributes

def aRX_QuadTest6(): Unit

The aRX_QuadTest6 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). This version performs FEATURE SELECTION (training-set, training-set).

The aRX_QuadTest6 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). This version performs FEATURE SELECTION (training-set, training-set).

runMain scalation.modeling.forecasting.aRX_QuadTest6

Attributes

def aRX_QuadTest7(): Unit

The aRX_QuadTest7 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). This version performs FEATURE SELECTION (training-set, testing-set).

The aRX_QuadTest7 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). This version performs FEATURE SELECTION (training-set, testing-set).

runMain scalation.modeling.forecasting.aRX_QuadTest7

Attributes

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

Attributes

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

Attributes

def aRX_Quad_DTest5(): Unit

The aRX_Quad_DTest5 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). This version performs feature selection.

The aRX_Quad_DTest5 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). This version performs feature selection.

runMain scalation.modeling.forecasting.aRX_Quad_DTest5

Attributes

def aRX_SRTest3(): Unit

The aRX_SRTest3 main function tests the ARX_SR class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_SRTest3 main function tests the ARX_SR 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_SRTest3

Attributes

def aRX_SRTest4(): Unit

The aRX_SRTest4 main function tests the ARX_SR 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_SRTest4 main function tests the ARX_SR 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_SRTest4

Attributes

def aRX_SRTest5(): Unit

The aRX_SRTest5 main function tests the ARX_SR 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_SRTest5 main function tests the ARX_SR 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_SRTest5

Attributes

def aRX_SRTest6(): Unit

The aRX_SRTest6 main function tests the ARX_SR 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). This version performs FEATURE SELECTION (training-set, training-set).

The aRX_SRTest6 main function tests the ARX_SR 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). This version performs FEATURE SELECTION (training-set, training-set).

runMain scalation.modeling.forecasting.aRX_SRTest6

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def aRX_SRTest7(): Unit

The aRX_SRTest7 main function tests the ARX_SR 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). This version performs FEATURE SELECTION (training-set, testing-set).

The aRX_SRTest7 main function tests the ARX_SR 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). This version performs FEATURE SELECTION (training-set, testing-set).

runMain scalation.modeling.forecasting.aRX_SRTest7

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def aRX_SR_DTest3(): Unit

The aRX_SR_DTest3 main function tests the ARX_SR_D class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_SR_DTest3 main function tests the ARX_SR_D class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRX_SR_DTest3

Attributes

def aRX_SR_DTest4(): Unit

The aRX_SR_DTest4 main function tests the ARX_SR_D class on real data: Forecasting COVID-19 using Train and Test (TnT). Test forecasts (h = 1 to hh steps ahead forecasts).

The aRX_SR_DTest4 main function tests the ARX_SR_D class on real data: Forecasting COVID-19 using Train and Test (TnT). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.aRX_SR_DTest4

Attributes

def aRX_SR_DTest5(): Unit

The aRX_SR_DTest5 main function tests the ARX_SR_D 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_SR_DTest5 main function tests the ARX_SR_D 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_SR_DTest5

Attributes

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

Attributes

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

Attributes

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

Attributes

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

Attributes

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

Attributes

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

Attributes

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

Attributes

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

Attributes

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

Attributes

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:

18.4998, 29.4024, 42.6675, 54.2169, 63.0084, 72.2290 ARY(1) 17.8595, 27.8544, 40.7768, 52.5351, 61.0972, 70.1973 ARY(2) 16.4745, 22.9627, 32.9324, 42.8385, 51.6931, 61.5292 ARY(3) 15.7400, 21.2937, 28.9582, 38.8393, 42.0740, 51.3300 ARY(4) 16.2315, 21.2230, 29.6593, 38.9380, 43.1389, 49.8287 ARY(5) 16.1056, 22.6427, 30.9964, 40.9072, 46.1625, 54.0774 ARY(6) 16.6737, 23.1236, 33.1328, 43.0148, 49.7683, 55.2326 ARY(7) 16.7242, 25.7990, 36.3484, 47.6660, 55.1432, 60.7600 ARY(8) 16.5522, 24.9271, 35.1184, 46.2757, 53.5985, 59.8611 ARY(9) 16.0764, 23.6507, 33.7168, 44.2123, 51.0500, 61.1110 ARY(10)

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, 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, 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)

18.7192, 28.0356, 38.0739, 46.8690, 54.2154, 60.8921 ARY_D(1) // FIX Bug -- too high 16.2602, 23.9446, 33.8763, 42.7548, 50.5601, 58.3793 ARY_D(2) 15.8284, 23.1419, 32.7795, 41.9619, 50.0289, 57.6217 ARY_D(3) 15.7065, 22.8376, 32.3402, 41.6187, 49.6103, 57.1951 ARY_D(4) 15.6925, 22.8577, 32.3599, 41.6423, 49.6253, 57.2027 ARY_D(5) 15.7054, 22.8457, 32.0807, 41.5924, 49.5162, 57.1057 ARY_D(6) 15.6252, 22.9759, 32.3061, 41.7185, 49.5916, 57.1693 ARY_D(7) 15.6665, 22.8945, 32.1515, 41.6596, 49.5256, 57.1121 ARY_D(8) 15.5888, 22.8066, 32.0457, 41.6402, 49.5181, 57.0147 ARY_D(9) 15.6341, 22.7900, 31.9169, 41.5769, 49.4211, 56.8442 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:

18.5012, 30.6359, 44.6907, 56.3819, 63.5357, 70.2028 ARY_D(1) 17.8594, 25.6887, 35.4256, 45.1790, 50.4527, 60.0111 ARY_D(2) 16.4715, 21.8016, 29.0381, 38.5104, 41.7722, 52.5948 ARY_D(3) 15.7366, 21.5619, 29.6182, 38.5514, 42.6748, 50.8053 ARY_D(4) 16.2315, 21.6376, 30.1303, 39.2472, 43.2712, 51.3975 ARY_D(5) 16.1058, 21.4914, 30.3555, 39.4806, 43.6360, 52.2788 ARY_D(6) 16.6739, 24.9328, 34.3104, 43.0995, 50.1624, 55.6230 ARY_D(7) 16.7248, 25.6616, 34.1789, 44.1880, 50.5008, 57.2136 ARY_D(8) 16.5494, 24.1130, 33.8701, 42.6320, 49.7175, 59.3071 ARY_D(9) 16.0705, 24.2940, 34.3169, 43.9710, 52.8199, 63.8433 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, 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, 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

Attributes

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, 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, 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:

16.8457, 24.4372, 32.1950, 40.6005, 47.7611, 53.9067 ARX(1, 1, 2) 14.0454, 18.9222, 26.7142, 35.7187, 43.8227, 50.6001 ARX(2, 2, 2) 13.8452, 18.1834, 24.5848, 32.9174, 40.9337, 47.7760 ARX(3, 2, 2) 13.7872, 17.8297, 23.8105, 31.4904, 39.1773, 46.0194 ARX(4, 2, 2) 13.7843, 17.8311, 23.8111, 31.4940, 39.1855, 46.0289 ARX(5, 2, 2) 13.8789, 17.9482, 24.0073, 31.5983, 38.8170, 45.1675 ARX(6, 2, 2) 13.8181, 17.9809, 24.0099, 31.6827, 38.9771, 45.5835 ARX(7, 2, 2) 13.9439, 17.6081, 23.7523, 31.3991, 38.6531, 44.9676 ARX(8, 2, 2) 13.7840, 17.6349, 23.8658, 31.1578, 38.6519, 44.6899 ARX(9, 2, 2) 13.8821, 17.6217, 23.6753, 31.0605, 38.6224, 44.6828 ARX(10, 2, 2)

18.7156, 28.5159, 37.2459, 45.8036, 51.9371, 56.6985 ARX(1, 1, 0) Agrees with ARY(p) 16.2587, 23.7072, 31.9906, 38.9940, 44.9856, 50.2418 ARX(2, 2, 0) 15.8240, 22.2659, 29.1170, 34.8505, 40.8580, 46.2113 ARX(3, 2, 0) 15.7020, 22.0134, 28.1169, 33.2691, 39.1811, 44.0750 ARX(4, 2, 0) 15.6875, 22.0198, 28.1429, 33.2990, 39.2397, 44.1700 ARX(5, 2, 0) 15.6982, 22.3197, 28.5239, 33.7716, 39.3099, 43.7038 ARX(6, 2, 0) 15.6186, 22.3438, 28.5437, 33.6401, 39.2926, 44.4411 ARX(7, 2, 0) 15.6595, 22.0566, 28.1782, 33.3346, 38.9921, 44.1955 ARX(8, 2, 0) 15.5823, 21.9463, 28.1055, 33.4000, 39.1296, 44.2667 ARX(9, 2, 0) 15.6267, 21.9089, 28.0047, 33.4205, 39.1586, 44.2015 ARX(10, 2, 0)

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:

11.0073, 19.3191, 26.2452, 35.9969, 47.8490, 58.9978 ARX(1, 1, 2) 10.4339, 19.6885, 25.2416, 35.4041, 47.6633, 58.1026 ARX(2, 2, 2) 10.0477, 19.6210, 25.8577, 35.9296, 47.6708, 58.2116 ARX(3, 2, 2) 9.34031, 17.6473, 23.3025, 33.0873, 46.0021, 57.6114 ARX(4, 2, 2) 10.5476, 17.6866, 23.1955, 33.2009, 45.4878, 57.6834 ARX(5, 2, 2) 11.5526, 18.6435, 24.7745, 35.5272, 44.6416, 57.7697 ARX(6, 2, 2) 11.3831, 17.8615, 23.0079, 33.1844, 44.4669, 57.2788 ARX(7, 2, 2) 11.1061, 17.4816, 22.1642, 33.2042, 45.1645, 57.6688 ARX(8, 2, 2) 11.3308, 18.0780, 23.5240, 34.5589, 46.3419, 58.6557 ARX(9, 2, 2) 11.4131, 19.5224, 25.7109, 36.8454, 49.5382, 60.9186 ARX(10, 2, 2)

18.4998, 29.4024, 42.6675, 54.2169, 63.0084, 72.2290 ARX(1, 1, 0) Agrees with ARY(p) 17.8595, 27.8544, 40.7768, 52.5351, 61.0972, 70.1973 ARX(2, 2, 0) 16.4745, 22.9627, 32.9324, 42.8385, 51.6931, 61.5292 ARX(3, 2, 0) 15.7400, 21.2937, 28.9582, 38.8393, 42.0740, 51.3300 ARX(4, 2, 0) 16.2315, 21.2230, 29.6593, 38.9380, 43.1389, 49.8287 ARX(5, 2, 0) 16.1056, 22.6427, 30.9964, 40.9072, 46.1625, 54.0774 ARX(6, 2, 0) 16.6737, 23.1236, 33.1328, 43.0148, 49.7683, 55.2326 ARX(7, 2, 0) 16.7242, 25.7990, 36.3484, 47.6660, 55.1432, 60.7600 ARX(8, 2, 0) 16.5522, 24.9271, 35.1184, 46.2757, 53.5985, 59.8611 ARX(9, 2, 0) 16.0764, 23.6507, 33.7168, 44.2123, 51.0500, 61.1110 ARX(10, 2, 0)

runMain scalation.modeling.forecasting.example_CovidTest16

Attributes

def example_CovidTest17(): Unit

The example_CovidTest17 main function tests the Example_Covid object. Uses In-Sample Testing, i.e., train and test on the same data. Runs Auto-Regressive, Exogenous ARX_D(p, q, n) models for several p values.

The example_CovidTest17 main function tests the Example_Covid object. Uses In-Sample Testing, i.e., train and test on the same data. Runs Auto-Regressive, Exogenous ARX_D(p, q, n) models for several p values.

16.8503, 25.2909, 35.4803, 44.6073, 52.2135, 59.4200 ARX_D(1, 1, 2) FIX Bug -- too high 14.0491, 20.5723, 30.7172, 40.1614, 48.7536, 56.8138 ARX_D(2, 2, 2) 13.8498, 20.2659, 30.2173, 39.6863, 48.4629, 56.3433 ARX_D(3, 2, 2) 13.7940, 19.9815, 29.8293, 39.3993, 48.0656, 55.9535 ARX_D(4, 2, 2) 13.7917, 19.9831, 29.8315, 39.4017, 48.0668, 55.9540 ARX_D(5, 2, 2) 13.8881, 20.0432, 29.5594, 39.4102, 47.9529, 55.8790 ARX_D(6, 2, 2) 13.8256, 20.2026, 29.7336, 39.5616, 48.0363, 55.9488 ARX_D(7, 2, 2) 13.9522, 20.0203, 29.5011, 39.4600, 47.9126, 55.8402 ARX_D(8, 2, 2) 13.7933, 19.8568, 29.2884, 39.3859, 47.7936, 55.7004 ARX_D(9, 2, 2) 13.8925, 19.8232, 29.0014, 39.2233, 47.6079, 55.3331 ARX_D(10, 2, 2)

runMain scalation.modeling.forecasting.example_CovidTest17

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def example_CovidTest18(): Unit

The example_CovidTest18 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Exogenous ARX_D(p, q, n) models for several p values.

The example_CovidTest18 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Exogenous ARX_D(p, q, n) models for several p values.

11.0164, 18.1939, 23.2358, 31.6475, 42.8253, 54.3245 ARX_D(1, 1, 2) 10.4521, 13.8955, 13.5074, 24.7088, 35.3807, 59.9904 ARX_D(2, 2, 2) 10.0793, 11.2167, 14.3570, 25.6690, 40.5868, 62.7553 ARX_D(3, 2, 2) 9.34877, 11.5869, 17.2668, 29.0107, 44.4698, 63.0348 ARX_D(4, 2, 2) 10.5457, 13.4293, 17.9561, 31.2434, 47.5865, 68.7770 ARX_D(5, 2, 2) 11.5562, 12.9348, 18.9845, 34.2571, 55.0142, 76.4654 ARX_D(6, 2, 2) 11.3872, 12.5172, 19.2266, 33.6051, 60.1889, 80.5061 ARX_D(7, 2, 2) 11.1091, 12.5290, 17.6071, 34.1205, 61.5160, 76.5128 ARX_D(8, 2, 2) 11.3466, 12.4147, 18.3370, 34.4428, 61.0988, 78.5466 ARX_D(9, 2, 2) 11.4265, 12.6378, 17.6644, 34.6527, 61.0367, 81.0210 ARX_D(10, 2, 2)

runMain scalation.modeling.forecasting.example_CovidTest18

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def example_CovidTest19(): Unit

The example_CovidTest19 main function tests the Example_Covid object. Uses In-Sample Testing, i.e., train and test on the same data. Runs Auto-Regressive, Exogenous ARX_Quad(p, q, n) models for several p values. Uses RidgeRegression with lambda = 0.1; y^pow with pow = 1.5.

The example_CovidTest19 main function tests the Example_Covid object. Uses In-Sample Testing, i.e., train and test on the same data. Runs Auto-Regressive, Exogenous ARX_Quad(p, q, n) models for several p values. Uses RidgeRegression with lambda = 0.1; y^pow with pow = 1.5.

16.6072, 24.4626, 32.6457, 40.8155, 47.9617, 54.1519 ARX_Quad(1, 1, 2) 14.8114, 18.7980, 24.6991, 33.2430, 42.0688, 49.9135 ARX_Quad(2, 2, 2) 14.3609, 18.4683, 24.3359, 32.3833, 40.2261, 48.3873 ARX_Quad(3, 2, 2) 14.1085, 18.5579, 24.8609, 32.6986, 40.6706, 48.4073 ARX_Quad(4, 2, 2) 13.8807, 18.3509, 24.6423, 32.0085, 40.2473, 47.6040 ARX_Quad(5, 2, 2) 13.6989, 18.4814, 24.6467, 32.5355, 40.7019, 48.2871 ARX_Quad(6, 2, 2) 13.7876, 18.4203, 24.6844, 32.2166, 40.6423, 48.0845 ARX_Quad(7, 2, 2) 13.9035, 17.5713, 23.6566, 31.4147, 40.4540, 48.7819 ARX_Quad(8, 2, 2) 13.7435, 17.6190, 23.4393, 31.4997, 40.7884, 49.3476 ARX_Quad(9, 2, 2) 13.8470, 17.8286, 22.5861, 30.3541, 38.8571, 47.1813 ARX_Quad(10, 2, 2)

runMain scalation.modeling.forecasting.example_CovidTest19

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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

Attributes

def example_CovidTest20(): Unit

The example_CovidTest20 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Exogenous ARX_Quad(p, q, n) models for several p values. Uses RidgeRegression with lambda = 0.1; y^pow with pow = 1.5.

The example_CovidTest20 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Exogenous ARX_Quad(p, q, n) models for several p values. Uses RidgeRegression with lambda = 0.1; y^pow with pow = 1.5.

11.8379, 18.8631, 25.3769, 34.6704, 46.0594, 58.3846 ARX_Quad(1, 1, 2) 11.4651, 13.9379, 17.4264, 29.5020, 41.4593, 69.7777 ARX_Quad(2, 2, 2) 10.9527, 12.5247, 16.9508, 28.3049, 44.0043, 66.2080 ARX_Quad(3, 2, 2) 10.0516, 12.8597, 17.9446, 30.4416, 45.4654, 64.7888 ARX_Quad(4, 2, 2) 10.9845, 13.3932, 18.5001, 32.2812, 48.1254, 69.1962 ARX_Quad(5, 2, 2) 11.1224, 13.8001, 20.4705, 36.9029, 56.0602, 82.4567 ARX_Quad(6, 2, 2) 11.8387, 13.8277, 21.2469, 37.6986, 63.8038, 88.8678 ARX_Quad(7, 2, 2) 11.5418, 14.6203, 21.4781, 40.8681, 70.1170, 88.3799 ARX_Quad(8, 2, 2) 12.3724, 14.8509, 21.5095, 40.9054, 70.3691, 87.8717 ARX_Quad(9, 2, 2) 12.6086, 15.7098, 21.6644, 41.5958, 69.4722, 82.9493 ARX_Quad(10, 2, 2)

runMain scalation.modeling.forecasting.example_CovidTest20

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def example_CovidTest21(): Unit

The example_CovidTest21 main function tests the Example_Covid object. Uses In-Sample Testing, i.e., train and test on the same data. Runs Auto-Regressive, Exogenous ARX_Quad_D(p, q, n) models for several p values.

The example_CovidTest21 main function tests the Example_Covid object. Uses In-Sample Testing, i.e., train and test on the same data. Runs Auto-Regressive, Exogenous ARX_Quad_D(p, q, n) models for several p values.

runMain scalation.modeling.forecasting.example_CovidTest21

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def example_CovidTest22(): Unit

The example_CovidTest22 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Exogenous ARX_Quad_D(p, q, n) models for several p values.

The example_CovidTest22 main function tests the Example_Covid object. Uses Train-n-Test Split (TnT) with Rolling Validation. Runs Auto-Regressive, Exogenous ARX_Quad_D(p, q, n) models for several p values.

runMain scalation.modeling.forecasting.example_CovidTest22

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def example_CovidTest3(): Unit

The example_CovidTest3 main function tests the Example_Covid object. Uses In-Sample Testing, 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, 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, 53.3361, 52.1227, 51.3005, 51.4569, 51.5041 Trend 24.2641, 25.5725, 39.2995, 45.9060, 54.4583, 60.3090 SMA -- FIX Bug h=2 too low 26.4055, 23.3947, 40.9707, 44.6394, 55.1448, 59.5280 WMA -- FIX Bug h=2 too low 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, 37.4800, 46.3173, 53.3245, 59.5733 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)

55.0263, 57.1038, 59.9686, 62.7341, 64.4922, 67.5687 Null 58.5433, 61.9389, 65.3934, 69.2238, 72.2127, 75.0520 Trend 9.30514, 20.1768, 31.9284, 44.6519, 56.0476, 67.5464 SMA -- FIX Bug 12.2955, 20.0054, 33.8672, 44.7494, 57.1694, 67.9005 WMA -- FIX Bug 33.3083, 44.2916, 54.1432, 64.0841, 73.5420, 80.7100 SES -- FIX Bug 18.1532, 27.2211, 40.3519, 52.3739, 62.5276, 73.6424 RW 17.8157, 26.6262, 39.4029, 51.5366, 61.7820, 73.3250 RWS 18.4659, 29.8363, 42.1980, 53.5928, 62.9734, 73.3153 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, 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, 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, 37.4800, 46.3173, 53.3245, 59.5733 AR(1) 16.3579, 24.7155, 33.0480, 40.1643, 46.8762, 53.2178 AR(2) 16.0114, 22.7408, 29.5631, 35.2773, 41.5856, 47.5716 AR(3) 15.8988, 22.5738, 28.5298, 33.3360, 39.1586, 44.3459 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

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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:

18.4659, 29.8363, 42.1980, 53.5928, 62.9734, 73.3153 AR(1) 16.7534, 25.7382, 38.5096, 49.3593, 57.9183, 69.7091 AR(2) 16.3630, 21.1490, 29.8750, 39.7999, 47.3691, 59.0869 AR(3) 15.0428, 20.1558, 29.3151, 38.2679, 43.0488, 51.3448 AR(4) 14.9448, 20.2989, 27.2780, 37.3160, 41.9003, 54.0098 AR(5) 13.9802, 19.7390, 27.2648, 35.6434, 42.2692, 50.8636 AR(6) 14.3902, 22.1659, 32.1102, 44.2965, 50.4653, 59.6916 AR(7) 15.0354, 24.8868, 35.9570, 49.9725, 55.2307, 62.9366 AR(8) 14.3458, 23.0047, 32.7333, 44.5037, 50.6380, 61.1755 AR(9) 14.0441, 23.9778, 35.8541, 48.1709, 53.5309, 63.7929 AR(10)

runMain scalation.modeling.forecasting.example_CovidTest6

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def example_CovidTest7(): Unit

The example_CovidTest7 main function tests the Example_Covid object. Uses In-Sample Testing, 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, 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

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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

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def example_CovidTest9(): Unit

The example_CovidTest9 main function tests the Example_Covid object. Uses In-Sample Testing, 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, 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:

18.7156, 28.5159, 37.2459, 45.8036, 51.9371, 56.6985 ARY(1) 16.2587, 23.7072, 31.9906, 38.9940, 44.9856, 50.2418 ARY(2) 15.8240, 22.2659, 29.1170, 34.8505, 40.8580, 46.2113 ARY(3) 15.7020, 22.0134, 28.1169, 33.2691, 39.1811, 44.0750 ARY(4) 15.6875, 22.0198, 28.1429, 33.2990, 39.2397, 44.1700 ARY(5) 15.6982, 22.3197, 28.5239, 33.7716, 39.3099, 43.7038 ARY(6) 15.6186, 22.3438, 28.5437, 33.6401, 39.2926, 44.4411 ARY(7) 15.6595, 22.0566, 28.1782, 33.3346, 38.9921, 44.1955 ARY(8) 15.5823, 21.9463, 28.1055, 33.4000, 39.1296, 44.2667 ARY(9) 15.6267, 21.9089, 28.0047, 33.4205, 39.1586, 44.2015 ARY(10)

runMain scalation.modeling.forecasting.example_CovidTest9

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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

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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

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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

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def example_ILITest10(): Unit

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

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

runMain scalation.modeling.forecasting.example_ILITest10

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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

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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

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def example_ILITest4(): Unit

The example_ILITest4 main function tests the Example_ILI 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_ILITest4 main function tests the Example_ILI 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_ILITest4

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def example_ILITest5(): Unit

The example_ILITest5 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_ILITest5 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 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 kalmanFilterTest(): Unit

The kalmanFilterTest main function is used to test the KalmanFilter class.

The kalmanFilterTest main function is used to test the KalmanFilter class.

Attributes

See also

en.wikipedia.org/wiki/Kalman_filter

runMain scalation.modeling.forecasting.kalmanFilterTest

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 a simple dataset using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The randomWalkTest5 main function tests the RandomWalk class on a simple dataset using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.randomWalkTest5

Attributes

def randomWalkTest6(): 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

Attributes

def randomWalkTest7(stm: Int): Unit

The randomWalkTest7 main function tests the RandomWalk class on small dataset. Test forecasts (h = 1 step ahead forecasts).

The randomWalkTest7 main function tests the RandomWalk class on small dataset. Test forecasts (h = 1 step ahead forecasts).

runMain scalation.modeling.forecasting.randomWalkTest7

Value parameters

stm

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

Attributes

def reportBest(mod: Forecaster_D, hh: Int, dir: String, toFile: Boolean = ..., ifTest: Boolean = ..., fsType: SelectionTech = ..., cross: String = ..., first: Int = ..., swap: Boolean = ...)(using qk: Int): Unit

Report the results for the best models given by feature selection to have in the model, returning the variables left and the new Quality of Fit (QoF) measures for all steps.

Report the results for the best models given by feature selection to have in the model, returning the variables left and the new Quality of Fit (QoF) measures for all steps.

Value parameters

cross

indicator to include the cross-validation/validation QoF measure (defaults to "none")

dir

the directory to save the report and qof

first

first variable to consider for elimination (default (1) assume intercept x_0 will be in any model)

fsType

the type of the feature selection to use

hh

the max forecasting horizon

ifTest

whether to use test set or full dataset

mod

the model to be evaluated

qk

index of Quality of Fit (QoF) to use for comparing quality

swap

whether to allow a swap step (swap out a feature for a new feature in one step)

toFile

whether to send to stdout or to a file

Attributes

def reportBestTest(): Unit

The reportBestTest main function fit the parameters and tests the ARX_D class on real data: Forecasting Covid19 using Train and Test (TnT). Backward/Stepwise feature selection Test forecasts (h = 1 to hh steps ahead forecasts).

The reportBestTest main function fit the parameters and tests the ARX_D class on real data: Forecasting Covid19 using Train and Test (TnT). Backward/Stepwise feature selection Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.reportBestTest

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 sGFilterTest(): Unit

The sGFilterTest main function tests the SGFilter class on real data: Smoothing Lake Levels data.

The sGFilterTest main function tests the SGFilter class on real data: Smoothing Lake Levels data.

Attributes

See also

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

runMain scalation.modeling.forecasting.sGFilterTest

def sGFilterTest2(): Unit

The sGFilterTest2 main function tests the SGFilter class on real data: Smoothing COVID-19 data.

The sGFilterTest2 main function tests the SGFilter class on real data: Smoothing COVID-19 data.

runMain scalation.modeling.forecasting.sGFilterTest2

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 simpleMovingAverage2Test(): Unit

The simpleMovingAverage2Test main function tests the SimpleMovingAverage2 class on real data: Forecasting Lake Levels using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleMovingAverage2Test main function tests the SimpleMovingAverage2 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.simpleMovingAverage2Test

def simpleMovingAverage2Test2(): Unit

The simpleMovingAverage2Test2 main function tests the SimpleMovingAverage2 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 simpleMovingAverage2Test2 main function tests the SimpleMovingAverage2 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.simpleMovingAverage2Test2

def simpleMovingAverage2Test3(): Unit

The simpleMovingAverage2Test3 main function tests the SimpleMovingAverage2 class on real data: Forecasting COVID-19 using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleMovingAverage2Test3 main function tests the SimpleMovingAverage2 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.simpleMovingAverage2Test3

Attributes

def simpleMovingAverage2Test4(): Unit

The simpleMovingAverage2Test4 main function tests the SimpleMovingAverage2 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 simpleMovingAverage2Test4 main function tests the SimpleMovingAverage2 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.simpleMovingAverage2Test4

Attributes

def simpleMovingAverage2Test5(): Unit

The simpleMovingAverage2Test5 main function tests the SimpleMovingAverage2 class on a simple dataset using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleMovingAverage2Test5 main function tests the SimpleMovingAverage2 class on a simple dataset using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.simpleMovingAverage2Test5

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 simpleMovingAverageTest5(): Unit

The simpleMovingAverageTest5 main function tests the SimpleMovingAverage class a simple data using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The simpleMovingAverageTest5 main function tests the SimpleMovingAverage class a simple data using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.simpleMovingAverageTest5

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 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

def weightedMovingAverageTest5(): Unit

The weightedMovingAverageTest5 main function tests the WeightedMovingAverage class on a simple dataset using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

The weightedMovingAverageTest5 main function tests the WeightedMovingAverage class on a simple dataset using In-Sample Testing (In-ST). Test forecasts (h = 1 to hh steps ahead forecasts).

runMain scalation.modeling.forecasting.weightedMovingAverageTest5

Attributes

Extensions

Extensions

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