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scalation/scalation/scalation.modeling/scalation.modeling.forecasting_old

scalation.modeling.forecasting_old

package scalation.modeling.forecasting_old

Members list

Type members

Classlikes

class AR(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

The AR class provides basic time series analysis capabilities for Auto-Regressive (AR) models. In an AR(p) model, p refers to the order of the Auto-Regressive components of the model. AR 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 past values of y:

The AR class provides basic time series analysis capabilities for Auto-Regressive (AR) models. In an AR(p) model, p refers to the order of the Auto-Regressive components of the model. AR 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 past values of y:

y_t+1 = δ + Σ[φ_j y_t-j] + e_t+1

where δ is a constant, φ is the auto-regressive coefficient vector, and e_t+1 is the new residual/error term.

Value parameters

hparam

the hyper-parameters

tt

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
trait Fit
trait FitM
trait Correlogram
trait Forecaster
trait Model
class Object
trait Matchable
class Any
Show all
object AR

The AR companion object provides factory methods for the AR class. Use SARIMA for hyper-parameters.

The AR companion object provides factory methods for the AR class. Use SARIMA for hyper-parameters.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
AR.type
class AR1MA(y: VectorD, tt: VectorD, hparam: HyperParameter, diffr: Boolean) extends Forecaster, Fit

The AR1MA class provides basic time-series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models. In an AR1MA(p, q) model, p and q refer to the order of the Auto-Regressive and Moving-Average components of the model; d=1 refers to the order of differencing. Works by taking the first difference and delegating to the ARMA class. Also works for d=0 (no differencing).

The AR1MA class provides basic time-series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models. In an AR1MA(p, q) model, p and q refer to the order of the Auto-Regressive and Moving-Average components of the model; d=1 refers to the order of differencing. Works by taking the first difference and delegating to the ARMA class. Also works for d=0 (no differencing).

Value parameters

diffr

whether to take a first difference (defaults to true)

hparam

the hyper-parameters

tt

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

y

the response vector (time-series data)

Attributes

Supertypes
trait Fit
trait FitM
trait Forecaster
trait Model
class Object
trait Matchable
class Any
Show all
class ARIMA(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Fit

The ARIMA class provides basic time series analysis capabilities for Auto- Regressive 'AR' Integrated 'I' Moving-Average 'MA' models. In an ARIMA(p, d, q) model, p and q refer to the order of the Auto-Regressive and Moving-Average components of the model; d refers to the order of differencing. Given time series data stored in vector y, its next value y_t = y(t) may be predicted based on prior values of y and its noise: y_t = δ + Σ(φ_i y_t-i) + Σ(θ_i e_t-i) + e_t where δ is a constant, φ is the auto-regressive coefficient vector, θ is the moving-average coefficient vector, and e is the noise vector.

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

Value parameters

hparam

the hyper-parameters

tt

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

y

the original input vector (time series data)

Attributes

Supertypes
trait Fit
trait FitM
trait Forecaster
trait Model
class Object
trait Matchable
class Any
Show all
object ARIMA_diff

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

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

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

Attributes

See also

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

Supertypes
class Object
trait Matchable
class Any
Self type
ARIMA_diff.type
class ARMA(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

The ARMA class provides basic time series analysis capabilities for Auto-Regressive, Moving-Average (ARMA) models. In an ARMA(p, q) model, p refers to the order of the Auto-Regressive components and q refers to the Moving-Average compoenest of the model. Given time series data stored in vector y, its next value y_t+1 = y(t+1) may be predicted based on past values of y and past shocks (differences in actual and forecasted values):

The ARMA class provides basic time series analysis capabilities for Auto-Regressive, Moving-Average (ARMA) models. In an ARMA(p, q) model, p refers to the order of the Auto-Regressive components and q refers to the Moving-Average compoenest of the model. Given time series data stored in vector y, its next value y_t+1 = y(t+1) may be predicted based on past values of y and past shocks (differences in actual and forecasted values):

y_t+1 = δ + Σ[φ_j y_t-j] + Σ[θ_j e_t-j] + e_t+1

where δ is a constant, φ is the auto-regressive coefficient vector, θ is the moving-average vector, and e_t+1 is the new residual/error/shock term.

Value parameters

hparam

the hyper-parameters

tt

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

y

the response vector (time series data)

Attributes

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

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARMA.type
class ARX(x: MatrixD, yy: VectorD, lags: Int, fname: Array[String], hparam: HyperParameter) extends Regression, ForecasterX

The ARX class supports regression for Time Series data. Multi-horizon forecasting supported via the RECURSIVE method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors,

The ARX class supports regression for Time Series data. Multi-horizon forecasting supported via the RECURSIVE method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors,

y_t = b dot x
where x = [1, y_{t-1}, y_{t-2}, ... y_{t-lags}]

Value parameters

fname

the feature/variable names

hparam

the hyper-parameters (use REGRESSION.hp for default)

lags

the maximum lag included (inclusive)

x

the input/predictor matrix built out of lags of y (and optionally from exogenous variables ex)

yy

the output/response vector trimmed to match x.dim (@see ARX object)

Attributes

Companion
object
Supertypes
trait ForecasterX
class Regression
trait Fit
trait FitM
trait Predictor
trait Model
class Object
trait Matchable
class Any
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object ARX

The ARX companion object provides factory methods.

The ARX companion object provides factory methods.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX.type
object ARX_MV

The ARX_MV object supports regression for Time Series data. Multi-horizon forecasting supported via the DIRECT method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

The ARX_MV object supports regression for Time Series data. Multi-horizon forecasting supported via the DIRECT method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

y_t = b dot x

where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
ARX_MV.type
class ARX_Quad(x: MatrixD, yy: VectorD, lags: Int, fname: Array[String], hparam: HyperParameter) extends Regression, ForecasterX

The ARX_Quad class supports quadratic regression for Time Series data. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors,

The ARX_Quad class supports quadratic regression for Time Series data. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors,

y_t = b dot x
where x = [ 1, y_{t-1}, y_{t-2}, ... y_{t-lag}, y_{t-1}^2, ...].

Value parameters

fname

the feature/variable names

hparam

the hyper-parameters (use Regression.hp for default)

lags

the maximum lag included (inclusive)

x

the input/predictor matrix built out of lags of y

yy

the output/response vector trimmed to match x.dim

Attributes

Companion
object
Supertypes
trait ForecasterX
class Regression
trait Fit
trait FitM
trait Predictor
trait Model
class Object
trait Matchable
class Any
Show all
object ARX_Quad

The ARX_Quad companion object provides factory methods.

The ARX_Quad companion object provides factory methods.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
ARX_Quad.type
final class ARX_QuadTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class ARX_QuadTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class ARX_QuadTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class ARX_QuadTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class ARX_QuadTest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class ARX_QuadTest6

Attributes

Supertypes
class Object
trait Matchable
class Any
object ARX_Quad_MV

The ARX_Quad_MV object supports quadratic regression for Time Series data. Multi-horizon forecasting supported via the DIRECT method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training. y_t = b dot x where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}]. Matrix x includes constant, linear and generalized quadratic terms (x^pw where pw defaults to 2.0). For example, a lower power such as 1.5 may work better for longer horizons (pw may be tuned).

The ARX_Quad_MV object supports quadratic regression for Time Series data. Multi-horizon forecasting supported via the DIRECT method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training. y_t = b dot x where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}]. Matrix x includes constant, linear and generalized quadratic terms (x^pw where pw defaults to 2.0). For example, a lower power such as 1.5 may work better for longer horizons (pw may be tuned).

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
trait Forecaster(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Model

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

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

Value parameters

hparam

the hyper-parameters for models extending this trait

tt

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

y

the response vector (time series data)

Attributes

Companion
object
Supertypes
trait Model
class Object
trait Matchable
class Any
Known subtypes
class AR
class AR1MA
class ARIMA
class ARMA
class NullModel
class QuadSpline
class RandomWalk
class TrendModel
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object Forecaster

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

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

Attributes

Companion
trait
Supertypes
class Object
trait Matchable
class Any
Self type
Forecaster.type
trait ForecasterX(lags: Int) extends Model

The ForecasterX trait provides a common framework for several forecasting models that use 1 ENDOGENOUS variable y and 0 or more EXOGENOUS variables xj. It provides methods for multi-horizon (1 to h) forecasting using the RECURSIVE technique. Forecasted values are produced only for the endogenous variable y. Lower case indicates actual values, while upper case is for forecasted values.

The ForecasterX trait provides a common framework for several forecasting models that use 1 ENDOGENOUS variable y and 0 or more EXOGENOUS variables xj. It provides methods for multi-horizon (1 to h) forecasting using the RECURSIVE technique. Forecasted values are produced only for the endogenous variable y. Lower case indicates actual values, while upper case is for forecasted values.

Y_t+1 = f(y_t, y_t-1, ... y_t-p+1, x0_t, x0_t-1, ... x0_t-p+1, x1_t, ...) Y_t+2 = f(Y_t+1, y_t, ... y_t-p+2, x0_t, x0_t-1, ... x0_t-p+1, x1_t, ...) ... Y_t+h = f(Y_t+1, y_t, ... y_t-p+2, x0_t, x0_t-1, ... x0_t-p+1, x1_t, ...)

Value parameters

lags

the lags (p) used for endogenous variable (e.g., 10 => use lags 1 to 10)

Attributes

See also

Forecaster - when there are no exogenous variables

Companion
object
Supertypes
trait Model
class Object
trait Matchable
class Any
Known subtypes
class ARX
class ARX_Quad
Show all
object ForecasterX

The ForecasterX companion object provides functions useful for classes extending the ForecasterX trait, i.e., forecasting models with multiple input variables.

The ForecasterX companion object provides functions useful for classes extending the ForecasterX trait, i.e., forecasting models with multiple input variables.

Attributes

Companion
trait
Supertypes
class Object
trait Matchable
class Any
Self type
class KalmanFilter(x0: VectorD, ff: MatrixD, hh: MatrixD, qq: MatrixD, rr: MatrixD, gg: MatrixD, u: VectorD)

The KalmanFilter class is used to fit state-space models. x_t = F x_t-1 + G u_t + w_t (State Equation) z_t = H x_t + v_t (Observation/Measurement Equation)

The KalmanFilter class is used to fit state-space models. x_t = F x_t-1 + G u_t + w_t (State Equation) z_t = H x_t + v_t (Observation/Measurement Equation)

Value parameters

ff

the state transition matrix (F)

gg

the optional control-input matrix (G)

hh

the observation matrix (H)

qq

the process noise covariance matrix (Q)

rr

the observation noise covariance matrix (R)

u

the optional control vector

x0

the initial state vector

Attributes

Supertypes
class Object
trait Matchable
class Any
class NullModel(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

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+1 = y(t+1) may be predicted based on prior value of y and its noise:

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+1 = y(t+1) may be predicted based on prior value of y and its noise:

y_t+1 = mu_y + e_t+1

where mu_y is the mean of y and e_t+1 is the new residual/error term.

Value parameters

hparam

the hyper-parameters (none => use null)

tt

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

y

the response vector (time-series data)

Attributes

Companion
object
Supertypes
trait Fit
trait FitM
trait Correlogram
trait Forecaster
trait Model
class Object
trait Matchable
class Any
Show all
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 QuadSpline(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

The QuadSpline class fits quadratic splines to time-series data that are equally spaced in time. A sliding window consisting of three data points is perfectly fit to a quadratic curve.

The QuadSpline class fits quadratic splines to time-series data that are equally spaced in time. A sliding window consisting of three data points is perfectly fit to a quadratic curve.

y_t = a + bt + ct^2

Note, slope matching and smoothness issues are ignored.

Value parameters

hparam

the hyper-parameters (none => use null)

tt

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

y

the response vector (time-series data)

Attributes

See also

wordsandbuttons.online/quadratic_splines_are_useful_too.html Any time point from t = 3 to the end of time series may be forecasted. FIX -- check the degrees of freedom (df)

Companion
object
Supertypes
trait Fit
trait FitM
trait Correlogram
trait Forecaster
trait Model
class Object
trait Matchable
class Any
Show all
object QuadSpline

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
QuadSpline.type
class RandomWalk(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

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

hparam

the hyper-parameters (none => use null)

tt

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

y

the response vector (time series data)

Attributes

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

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
RandomWalk.type
class RegressionTreeGB4TS(x: MatrixD, yy: VectorD, lags: Int, fname: Array[String], hparam: HyperParameter) extends RegressionTreeGB, ForecasterX

The RegressionTreeGB4TS class supports Gradient Boosting for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

The RegressionTreeGB4TS class supports Gradient Boosting for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

y_t = f(x)

where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].

Value parameters

fname

the feature/variable names

hparam

the hyper-parameters (use REGRESSION.hp for default)

lags

the maximum lag included (inclusive)

x

the input/predictor matrix built out of lags of y (and optionally from exogenous variables ex)

yy

the output/response vector trimmed to match x.dim (@see ARX object)

Attributes

Companion
object
Supertypes
trait ForecasterX
trait Fit
trait FitM
trait Predictor
trait Model
class Object
trait Matchable
class Any
Show all
object RegressionTreeGB4TS

The RegressionTreeGB4TS companion object provides factory methods.

The RegressionTreeGB4TS companion object provides factory methods.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
object RegressionTreeGB4TS2

The RegressionTreeGB4TS2 object supports Gradient Boosting for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

The RegressionTreeGB4TS2 object supports Gradient Boosting for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

y_t = f(x)

where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
class RegressionTreeMT4TS(x: MatrixD, yy: VectorD, lags: Int, fname: Array[String], hparam: HyperParameter) extends RegressionTreeMT, ForecasterX

The RegressionTreeMT4TS class supports Model Trees for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

The RegressionTreeMT4TS class supports Model Trees for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

y_t = f(x)

where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].

Value parameters

fname

the feature/variable names

hparam

the hyper-parameters (use RegressionTree.hp for default)

lags

the maximum lag included (inclusive)

x

the input/predictor matrix built out of lags of y (and optionally from exogenous variables ex)

yy

the output/response vector trimmed to match x.dim (@see ARX object)

Attributes

Companion
object
Supertypes
trait ForecasterX
trait Fit
trait FitM
trait Predictor
trait Model
class Object
trait Matchable
class Any
Show all
object RegressionTreeMT4TS

The RegressionTreeMT4TS companion object provides factory methods.

The RegressionTreeMT4TS companion object provides factory methods.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
class RegressionTreeRF4TS(x: MatrixD, yy: VectorD, lags: Int, fname: Array[String], use_fb: Boolean, hparam: HyperParameter) extends RegressionTreeRF, ForecasterX

The RegressionTreeRF4TS class supports Random Forest for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

The RegressionTreeRF4TS class supports Random Forest for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

y_t = f(x)

where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].

Value parameters

fname

the feature/variable names

hparam

the hyper-parameters (use RegressionTree.hp for default)

lags

the maximum lag included (inclusive)

use_fb

whether to use feature bagging (select subsets of the features)

x

the input/predictor matrix built out of lags of y (and optionally from exogenous variables ex)

yy

the output/response vector trimmed to match x.dim (@see ARX object)

Attributes

Companion
object
Supertypes
trait ForecasterX
trait Fit
trait FitM
trait Predictor
trait Model
class Object
trait Matchable
class Any
Show all
object RegressionTreeRF4TS

The RegressionTreeRF4TS companion object provides factory methods.

The RegressionTreeRF4TS companion object provides factory methods.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
class RegressionTreeRF_MT4TS(x: MatrixD, yy: VectorD, lags: Int, fname: Array[String], use_fb: Boolean, hparam: HyperParameter) extends RegressionTreeRF, ForecasterX

The RegressionTreeRF_MT4TS class supports Random Forest for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

The RegressionTreeRF_MT4TS class supports Random Forest for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.

y_t = f(x)

where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].

Value parameters

fname

the feature/variable names

hparam

the hyper-parameters (use RegressionTree.hp for default)

lags

the maximum lag included (inclusive)

use_fb

whether to use feature bagging (select subsets of the features)

x

the input/predictor matrix built out of lags of y (and optionally from exogenous variables ex)

yy

the output/response vector trimmed to match x.dim (@see ARX object)

Attributes

Companion
object
Supertypes
trait ForecasterX
trait Fit
trait FitM
trait Predictor
trait Model
class Object
trait Matchable
class Any
Show all
object RegressionTreeRF_MT4TS

The RegressionTreeRF_MT4TS companion object provides factory methods.

The RegressionTreeRF_MT4TS companion object provides factory methods.

Attributes

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

The RollingValidation object provides rolling-validation, where a full dataset is divided into a training set followed by a testing set. Retraining is done as the algorithm rolls through the testing set making out-of-sample predictions/forecasts to keep the parameters from becoming stale. For example, with TE_RATIO = 0.5 and m = 1000 it works as follows: tr(ain) 0 to 499, te(st) 500 to 999 Re-training occurs according to the retraining cycle rc, e.g., rc = 10 implies that retraining would occurs after every 10 forecasts or 50 times for this example.

The RollingValidation object provides rolling-validation, where a full dataset is divided into a training set followed by a testing set. Retraining is done as the algorithm rolls through the testing set making out-of-sample predictions/forecasts to keep the parameters from becoming stale. For example, with TE_RATIO = 0.5 and m = 1000 it works as follows: tr(ain) 0 to 499, te(st) 500 to 999 Re-training occurs according to the retraining cycle rc, e.g., rc = 10 implies that retraining would occurs after every 10 forecasts or 50 times for this example.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
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
class SimpleExpSmoothing(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

The SimpleExpSmoothing class provide very basic time series analysis using Simple Exponential Smoothing models. The forecasted value is the weighted average the latest value y_t and the latest smoothed value s_t. The smoothing parameter α in [0, 1] causes the contributions of older values to decay exponentially.

The SimpleExpSmoothing class provide very basic time series analysis using Simple Exponential Smoothing models. The forecasted value is the weighted average the latest value y_t and the latest smoothed value s_t. The smoothing parameter α in [0, 1] causes the contributions of older values to decay exponentially.

Value parameters

hparam

the hyper-parameters

tt

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

y

the response vector (original time series data)

Attributes

See also

Smoothing Equation in section 7.1. s_t+1 = α y_t + (1 - α) s_t smoothing equation yf_t+1 = s_t+1 forecast equation where vector s is the smoothed version of vector y.

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

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
class SimpleMovingAverage(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

The SimpleMovingAverage class provides basic time series analysis capabilities. For a SimpleMovingAverage model with the time series data stored in vector y, the next value y_t+1 = y(t+1) may be predicted based on the mean of the q prior values of y:

The SimpleMovingAverage class provides basic time series analysis capabilities. For a SimpleMovingAverage model with the time series data stored in vector y, the next value y_t+1 = y(t+1) may be predicted based on the mean of the q prior values of y:

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

where e_t+1 is the new residual/error term and q' = q-1.

Value parameters

hparam

the hyper-parameters

tt

the time points, if needed

y

the response vector (time series data)

Attributes

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

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
class TrendModel(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

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+1 = y(t+1) may be predicted based on a linear function of time 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+1 = y(t+1) may be predicted based on a linear function of time t:

y_t+1 = b0 + b1 (t+1) + e_t+1

where b0 is the intercept, b1 is the slope and e_t+1 is the new residual/error term.

Value parameters

hparam

the hyper-parameters (none => use null)

tt

the time vector (time index may suffice)

y

the response vector (time-series data)

Attributes

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

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

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

Attributes

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

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

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

Value parameters

lags

the number of lags to use

lagsType

default lags value long or short time-series

nobs

the number of observations (length of time-series)

testName

the name of test, e.g., KPSS

trend

type of trend to test for

validTrends

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

Attributes

See also

github.com/olmallet81/URT.

Supertypes
class Object
trait Matchable
class Any
object VAR

The VAR object supports regression for Multivariate Time Series data. Given a response matrix y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training. y_t = b dot x where x = [y_{t-1}, y_{t-2}, ... y_{t-lag}].

The VAR object supports regression for Multivariate Time Series data. Given a response matrix y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training. y_t = b dot x where x = [y_{t-1}, y_{t-2}, ... y_{t-lag}].

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
VAR.type
class WeightedMovingAverage(y: VectorD, tt: VectorD, hparam: HyperParameter) extends Forecaster, Correlogram, Fit

The WeightedMovingAverage class provides basic time series analysis capabilities. For a WeightedMovingAverage model with the time series data stored in vector y, the next value y_t+1 = y(t+1) may be predicted based on the weighted-average of the past q values of y:

The WeightedMovingAverage class provides basic time series analysis capabilities. For a WeightedMovingAverage model with the time series data stored in vector y, the next value y_t+1 = y(t+1) may be predicted based on the weighted-average of the past q values of y:

y_t+1 = weight-average (y_t, ..., y_t-q') + e_t+1

where e_t+1 is the new residual/error term and q' = q-1. The hyper-parameter u selects between flat (u = 0), linear weights (u = 1) or comninations of both.

Value parameters

hparam

the hyper-parameters

tt

the time points, if needed

y

the response vector (time series data)

Attributes

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

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

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

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
final class aR1MATest

Attributes

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class Object
trait Matchable
class Any
final class aR1MATest2

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class Object
trait Matchable
class Any
final class aR1MATest3

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class Object
trait Matchable
class Any
final class aR1MATest4

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class Object
trait Matchable
class Any
final class aR1MATest5

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class Object
trait Matchable
class Any
final class aRIMATest

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class Object
trait Matchable
class Any
final class aRIMATest2

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class Object
trait Matchable
class Any
final class aRIMATest3

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class Object
trait Matchable
class Any
final class aRIMATest4

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class Object
trait Matchable
class Any
final class aRIMA_diffTest

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class Object
trait Matchable
class Any
final class aRMATest

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class Object
trait Matchable
class Any
final class aRMATest2

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class Object
trait Matchable
class Any
final class aRMATest3

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class Object
trait Matchable
class Any
final class aRMATest4

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class Object
trait Matchable
class Any
final class aRMATest5

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class Object
trait Matchable
class Any
final class aRMATest6

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class Object
trait Matchable
class Any
final class aRMATest7

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class Object
trait Matchable
class Any
final class aRTest

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class Object
trait Matchable
class Any
final class aRTest2

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class Object
trait Matchable
class Any
final class aRTest3

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class Object
trait Matchable
class Any
final class aRTest4

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class Object
trait Matchable
class Any
final class aRTest5

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class Object
trait Matchable
class Any
final class aRTest6

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class Object
trait Matchable
class Any
final class aRTest7

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class Object
trait Matchable
class Any
final class aRXTest

Attributes

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class Object
trait Matchable
class Any
final class aRXTest2

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class Object
trait Matchable
class Any
final class aRXTest3

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class Object
trait Matchable
class Any
final class aRXTest4

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class Object
trait Matchable
class Any
final class aRXTest5

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class Object
trait Matchable
class Any
final class aRXTest6

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class Object
trait Matchable
class Any
final class aRXTest7

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class Object
trait Matchable
class Any
final class aRX_MVTest

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class Object
trait Matchable
class Any
final class aRX_MVTest2

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class Object
trait Matchable
class Any
final class aRX_MVTest3

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class Object
trait Matchable
class Any
final class aRX_MVTest4

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class Object
trait Matchable
class Any
final class aRX_MVTest5

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class Object
trait Matchable
class Any
final class aRX_MVTest6

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class Object
trait Matchable
class Any
final class aRX_Quad_MVTest

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class Object
trait Matchable
class Any
final class aRX_Quad_MVTest2

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class Object
trait Matchable
class Any
final class aRX_Quad_MVTest3

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class Object
trait Matchable
class Any
final class aRX_Quad_MVTest4

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class Object
trait Matchable
class Any
final class aRX_Quad_MVTest5

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class Object
trait Matchable
class Any
final class aRX_Quad_MVTest6

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class Object
trait Matchable
class Any
final class buildTensor4TSTest

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class Object
trait Matchable
class Any
final class kalmanFilterTest

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class Object
trait Matchable
class Any
final class nullModelTest

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class Object
trait Matchable
class Any
final class nullModelTest2

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class Object
trait Matchable
class Any
final class nullModelTest3

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class Object
trait Matchable
class Any
final class quadSplineTest

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class Object
trait Matchable
class Any
final class quadSplineTest2

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class Object
trait Matchable
class Any
final class quadSplineTest3

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class Object
trait Matchable
class Any
final class quadSplineTest4

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class Object
trait Matchable
class Any
final class randomWalkTest

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class Object
trait Matchable
class Any
final class randomWalkTest2

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class Object
trait Matchable
class Any
final class randomWalkTest3

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class Object
trait Matchable
class Any
final class randomWalkTest4

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class Object
trait Matchable
class Any
final class regressionTreeGB4TS2Test

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class Object
trait Matchable
class Any
final class regressionTreeGB4TS2Test2

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class Object
trait Matchable
class Any
final class regressionTreeGB4TS2Test3

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class Object
trait Matchable
class Any
final class regressionTreeGB4TS2Test4

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class Object
trait Matchable
class Any
final class regressionTreeGB4TS2Test5

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class Object
trait Matchable
class Any
final class regressionTreeGB4TS2Test6

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class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest

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class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest2

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class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest3

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class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest4

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class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest5

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class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest6

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class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest7

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class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest

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class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest2

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class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest3

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class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest4

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class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest5

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class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest6

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class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest7

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class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest

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class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest2

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class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest3

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class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest4

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class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest5

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class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest6

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class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest7

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class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest

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class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest2

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class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest3

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class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest4

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class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest5

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class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest6

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class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest7

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class Object
trait Matchable
class Any
final class rollingValidationTest

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class Object
trait Matchable
class Any
final class rollingValidationTest2

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class Object
trait Matchable
class Any
final class rollingValidationTest3

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class Object
trait Matchable
class Any
final class rollingValidationTest4

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class Object
trait Matchable
class Any
final class simpleExpSmoothingTest

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class Object
trait Matchable
class Any
final class simpleExpSmoothingTest2

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class Object
trait Matchable
class Any
final class simpleExpSmoothingTest3

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class Object
trait Matchable
class Any
final class simpleExpSmoothingTest4

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class Object
trait Matchable
class Any
final class simpleExpSmoothingTest5

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class Object
trait Matchable
class Any
final class simpleMovingAverageTest

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class Object
trait Matchable
class Any
final class simpleMovingAverageTest2

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class Object
trait Matchable
class Any
final class simpleMovingAverageTest3

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class Object
trait Matchable
class Any
final class simpleMovingAverageTest4

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class Object
trait Matchable
class Any
final class simpleMovingAverageTest5

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class Object
trait Matchable
class Any
final class stationaryTest

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class Object
trait Matchable
class Any
final class stationaryTest2

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class Object
trait Matchable
class Any
final class stationaryTest3

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class Object
trait Matchable
class Any
final class trendModelTest

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class Object
trait Matchable
class Any
final class trendModelTest2

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class Object
trait Matchable
class Any
final class trendModelTest3

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class Object
trait Matchable
class Any
final class varTest

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class Object
trait Matchable
class Any
final class varTest2

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class Object
trait Matchable
class Any
final class varTest3

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class Object
trait Matchable
class Any
final class varTest4

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class Object
trait Matchable
class Any
final class varTest5

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class Object
trait Matchable
class Any
final class varTest6

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class Object
trait Matchable
class Any
final class weightedMovingAverageTest

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class Object
trait Matchable
class Any
final class weightedMovingAverageTest2

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class Object
trait Matchable
class Any
final class weightedMovingAverageTest3

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class Object
trait Matchable
class Any
final class weightedMovingAverageTest4

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class Object
trait Matchable
class Any
final class weightedMovingAverageTest5

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class Object
trait Matchable
class Any

Types

type CriticalValues = HashMap[Int, VectorD]

Value members

Concrete methods

def ARX_QuadTest(): Unit

The ARX_QuadTest main function tests the ARX_Quad class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The ARX_QuadTest main function tests the ARX_Quad class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.ARX_QuadTest

Attributes

def ARX_QuadTest2(): Unit

The ARX_QuadTest2 main function tests the ARX_Quad class on real data: Forecasting lake levels.

The ARX_QuadTest2 main function tests the ARX_Quad class on real data: Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.ARX_QuadTest2

def ARX_QuadTest3(): Unit

The ARX_QuadTest3 main function tests the ARX_Quad class on real data: Forecasting COVID-19. Does In-Sample Testing on Endogenous variable.

The ARX_QuadTest3 main function tests the ARX_Quad class on real data: Forecasting COVID-19. Does In-Sample Testing on Endogenous variable.

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 Weekly Data. Does In-Sample Testing on endogenous and exogenous variables. Stepsise gives: 0, 19, 40, 37, 60, 17, 15, 53, 5, 20, 50, 49, 48, 47, 18, 9, 6 best R^2-bar Stepsise gives: 0, 19, 40, 37, 60, 17, 15, 53, 5, 20, 50, 49, 48, 47 best sMAPE

The ARX_QuadTest4 main function tests the ARX_Quad class on real data: Forecasting COVID-19 Weekly Data. Does In-Sample Testing on endogenous and exogenous variables. Stepsise gives: 0, 19, 40, 37, 60, 17, 15, 53, 5, 20, 50, 49, 48, 47, 18, 9, 6 best R^2-bar Stepsise gives: 0, 19, 40, 37, 60, 17, 15, 53, 5, 20, 50, 49, 48, 47 best sMAPE

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 Weekly Data. Does TnT Testing on endogenous and exogenous variables. Determine the terms to include in the model for TnT from using Stepwise on In-Sample. Stepsise gives: 0, 19, 40, 37, 60, 17, 15, 53, 5, 20, 50, 49, 48, 47, 18, 9, 6 best R^2-bar Stepsise gives: 0, 19, 40, 37, 60, 17, 15, 53, 5, 20, 50, 49, 48, 47 best sMAPE

The ARX_QuadTest5 main function tests the ARX_Quad class on real data: Forecasting COVID-19 Weekly Data. Does TnT Testing on endogenous and exogenous variables. Determine the terms to include in the model for TnT from using Stepwise on In-Sample. Stepsise gives: 0, 19, 40, 37, 60, 17, 15, 53, 5, 20, 50, 49, 48, 47, 18, 9, 6 best R^2-bar Stepsise gives: 0, 19, 40, 37, 60, 17, 15, 53, 5, 20, 50, 49, 48, 47 best sMAPE

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 Weekly Data. Does Rolling Validation on endogenous and exogenous variables. Determine the terms to include in the model HOW?

The ARX_QuadTest6 main function tests the ARX_Quad class on real data: Forecasting COVID-19 Weekly Data. Does Rolling Validation on endogenous and exogenous variables. Determine the terms to include in the model HOW?

runMain scalation.modeling.forecasting.ARX_QuadTest6

Attributes

def aR1MATest(): Unit

The aR1MATest main function tests the AR1MA class on simulated data. Test predictions (one step ahead forecasts).

The aR1MATest main function tests the AR1MA class on simulated data. Test predictions (one step ahead forecasts).

Attributes

See also

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

runMain scalation.modeling.forecasting.aR1MATest

def aR1MATest2(): Unit

The aR1MATest2 main function tests the AR1MA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts) with no differencing

The aR1MATest2 main function tests the AR1MA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts) with no differencing

Attributes

See also

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

aRMATest2

runMain scalation.modeling.forecasting.aR1MATest2

def aR1MATest3(): Unit

The aR1MATest3 main function tests the AR1MA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts) taking one difference.

The aR1MATest3 main function tests the AR1MA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts) taking one difference.

Attributes

See also

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

runMain scalation.modeling.forecasting.aR1MATest3

def aR1MATest4(): Unit

The aR1MATest4 main function tests the AR1MA class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts) for several values of p and q.

The aR1MATest4 main function tests the AR1MA class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts) for several values of p and q.

Attributes

See also

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

aRMATest5

runMain scalation.modeling.forecasting.aR1MATest4

def aR1MATest5(): Unit

The aR1MATest5 main function tests the AR1MA class on real data: Forecasting COVID-19.

The aR1MATest5 main function tests the AR1MA class on real data: Forecasting COVID-19.

runMain scalation.modeling.forecasting.aR1MATest5

Attributes

def aRIMATest(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.aRIMATest

def aRIMATest2(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.aRIMATest2

def aRIMATest3(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.aRIMATest3

def aRIMATest4(): Unit

The aRIMATest4 main function tests the ARIMA class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts) for several values of p and q.

The aRIMATest4 main function tests the ARIMA class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts) for several values of p and q.

Attributes

See also

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

aRMATest5

runMain scalation.modeling.forecasting.aRIMATest4

def aRIMA_diffTest(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.aRIMA_diffTest

def aRMATest(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.aRTest

def aRMATest2(): Unit

The aRMATest2 main function tests the ARMA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts).

The aRMATest2 main function tests the ARMA class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts).

Attributes

See also

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

aR1MATest2

runMain scalation.modeling.forecasting.aRMATest2

def aRMATest3(): Unit

The aRMATest3 main function tests the ARMA class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts).

The aRMATest3 main function tests the ARMA class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts).

Attributes

See also

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

runMain scalation.modeling.forecasting.aRMATest3

def aRMATest4(): Unit

The aRMATest4 main function tests the ARMA class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts) for several values of p and q.

The aRMATest4 main function tests the ARMA class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts) for several values of p and q.

Attributes

See also

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

runMain scalation.modeling.forecasting.aRMATest4

def aRMATest5(): Unit

The aRMATest5 main function tests the ARMA class on real data: Forecasting lake levels. This test looks at the velocity series (first differences). Test predictions (one step ahead forecasts).

The aRMATest5 main function tests the ARMA class on real data: Forecasting lake levels. This test looks at the velocity series (first differences). Test predictions (one step ahead forecasts).

Attributes

See also

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

aR1MATest4

runMain scalation.modeling.forecasting.aRMATest5

def aRMATest6(): Unit

The aRMATest6 main function tests the ARMA class on real data: Forecasting COVID-19.

The aRMATest6 main function tests the ARMA class on real data: Forecasting COVID-19.

runMain scalation.modeling.forecasting.aRMATest6

Attributes

def aRMATest7(): Unit

The aRMATest6 main function tests the AR class on real data: Forecasting Weekly Covid-19. Test forecasts (1 to h steps ahead forecasts). Try multiple values for p.

The aRMATest6 main function tests the AR class on real data: Forecasting Weekly Covid-19. Test forecasts (1 to h steps ahead forecasts). Try multiple values for p.

runMain scalation.modeling.forecasting.aRMATest7

Attributes

def aRTest(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.aRTest

def aRTest2(): Unit

The aRTest2 main function tests the AR class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts).

The aRTest2 main function tests the AR class on real data: Forecasting lake levels. Test predictions (one step 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 lake levels. Test forecasts (1 to h steps ahead forecasts).

The aRTest3 main function tests the AR class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts).

Attributes

See also

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

runMain scalation.modeling.forecasting.aRTest3

def aRTest4(): Unit

The aRTest4 main function tests the AR class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts). Try multiple values for p.

The aRTest4 main function tests the AR class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts). Try multiple values for p.

Attributes

See also

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

runMain scalation.modeling.forecasting.aRTest4

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 aRTest6(): Unit

The aRTest6 main function tests the AR class on real data: Forecasting Weekly Covid-19. Test forecasts (1 to h steps ahead forecasts). Try multiple values for p.

The aRTest6 main function tests the AR class on real data: Forecasting Weekly Covid-19. Test forecasts (1 to h steps ahead forecasts). Try multiple values for p.

runMain scalation.modeling.forecasting.aRTest6

Attributes

def aRTest7(): Unit

The aRTest7 main function tests the AR class on simulated data. Test predictions (one step ahead forecasts).

The aRTest7 main function tests the AR class on simulated data. Test predictions (one step ahead forecasts).

runMain scalation.modeling.forecasting.aRTest7

Attributes

def aRXTest(): Unit

The aRXTest main function tests the ARX class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The aRXTest main function tests the ARX class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.aRXTest

Attributes

def aRXTest2(): Unit

The aRXTest2 main function tests the ARX class on real data: Forecasting lake levels.

The aRXTest2 main function tests the ARX class on real data: Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.aRXTest2

def aRXTest3(): Unit

The aRXTest3 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

The aRXTest3 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.aRXTest3

Attributes

def aRXTest4(): Unit

The aRXTest4 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

The aRXTest4 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.aRXTest4

Attributes

def aRXTest5(): Unit

The aRXTest5 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

The aRXTest5 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

runMain scalation.modeling.forecasting.aRXTest5

Attributes

def aRXTest6(): Unit

The aRXTest6 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

The aRXTest6 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

runMain scalation.modeling.forecasting.aRXTest6

Attributes

def aRXTest7(): Unit

The aRXTest7 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Preliminary investigation of Symbolic Regression.

The aRXTest7 main function tests the ARX class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Preliminary investigation of Symbolic Regression.

runMain scalation.modeling.forecasting.aRXTest7

Attributes

def aRX_MVTest(): Unit

The aRX_MVTest main function tests the ARX_MV class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The aRX_MVTest main function tests the ARX_MV class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.aRX_MVTest

Attributes

def aRX_MVTest2(): Unit

The aRX_MVTest2 main function tests the ARX_MV class on real data: Forecasting lake levels.

The aRX_MVTest2 main function tests the ARX_MV class on real data: Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.aRX_MVTest2

def aRX_MVTest3(): Unit

The aRX_MVTest3 main function tests the ARX_MV class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

The aRX_MVTest3 main function tests the ARX_MV class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.aRX_MVTest3

Attributes

def aRX_MVTest4(): Unit

The aRX_MVTest4 main function tests the ARX_MV class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

The aRX_MVTest4 main function tests the ARX_MV class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.aRX_MVTest4

Attributes

def aRX_MVTest5(): Unit

The aRX_MVTest5 main function tests the ARX_MV class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

The aRX_MVTest5 main function tests the ARX_MV class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

runMain scalation.modeling.forecasting.aRX_MVTest5

Attributes

def aRX_MVTest6(): Unit

The aRX_MVTest6 main function tests the ARX_MV class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

The aRX_MVTest6 main function tests the ARX_MV class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

runMain scalation.modeling.forecasting.aRX_MVTest6

Attributes

def aRX_Quad_MVTest(): Unit

The aRX_Quad_MVTest main function tests the ARX_Quad_MV object. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The aRX_Quad_MVTest main function tests the ARX_Quad_MV object. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.aRX_Quad_MVTest

Attributes

def aRX_Quad_MVTest2(): Unit

The aRX_Quad_MVTest2 main function tests the ARX_Quad_MV class on real data: Forecasting lake levels. Uses quadratic regression.

The aRX_Quad_MVTest2 main function tests the ARX_Quad_MV class on real data: Forecasting lake levels. Uses quadratic regression.

Attributes

See also

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

runMain scalation.modeling.forecasting.aRX_Quad_MVTest2

def aRX_Quad_MVTest3(): Unit

The aRX_Quad_MVTest3 main function tests the ARX_Quad_MV class on real data: Forecasting COVID-19 Weekly Data. Uses quadratic regression, In-Sample Testing using endogenous variable.

The aRX_Quad_MVTest3 main function tests the ARX_Quad_MV class on real data: Forecasting COVID-19 Weekly Data. Uses quadratic regression, In-Sample Testing using endogenous variable.

runMain scalation.modeling.forecasting.aRX_Quad_MVTest3

Attributes

def aRX_Quad_MVTest4(): Unit

The aRX_Quad_MVTest4 main function tests the ARX_Quad_MV class on real data: Forecasting COVID-19 Weekly Data. Uses quadratic regression, In-Sample Testing using endogenous and exogeneous variables.

The aRX_Quad_MVTest4 main function tests the ARX_Quad_MV class on real data: Forecasting COVID-19 Weekly Data. Uses quadratic regression, In-Sample Testing using endogenous and exogeneous variables.

runMain scalation.modeling.forecasting.aRX_Quad_MVTest4

Attributes

def aRX_Quad_MVTest5(): Unit

The aRX_Quad_MVTest5 main function tests the ARX_Quad_MV class on real data: Forecasting COVID-19 Weekly Data. Uses Quadratic Regression. Does TnT Testing on endogenous and exogenous variables. Determine the terms to include in the model for TnT from using Stepwise on In-Sample.

The aRX_Quad_MVTest5 main function tests the ARX_Quad_MV class on real data: Forecasting COVID-19 Weekly Data. Uses Quadratic Regression. Does TnT Testing on endogenous and exogenous variables. Determine the terms to include in the model for TnT from using Stepwise on In-Sample.

runMain scalation.modeling.forecasting.aRX_Quad_MVTest5

Attributes

def aRX_Quad_MVTest6(): Unit

The aRX_Quad_MVTest6 main function tests the ARX_Quad_MV class on real data: Forecasting COVID-19 Weekly Data. Uses Quadratic Regression. Does TnT Testing on endogenous and exogenous variables. Determine the terms to include in the model for TnT from using Stepwise on In-Sample.

The aRX_Quad_MVTest6 main function tests the ARX_Quad_MV class on real data: Forecasting COVID-19 Weekly Data. Uses Quadratic Regression. Does TnT Testing on endogenous and exogenous variables. Determine the terms to include in the model for TnT from using Stepwise on In-Sample.

runMain scalation.modeling.forecasting.aRX_Quad_MVTest6

Attributes

def buildMatrix4TS(y: VectorD, lags: Int): (MatrixD, VectorD)

Given a response vector y, build and return (1) an input/predictor MATRIX xx and (2) an output/single-horizon output/response VECTOR yy. Used by Single-Variate forecast models such as ARX. that use RECURSIVE multi-horizon forecasting. The first response can't be predicted due to missing past values. Therefore the number of rows in xx and yy is reduced to "y.dim-1" (time 0 cut out).

Given a response vector y, build and return (1) an input/predictor MATRIX xx and (2) an output/single-horizon output/response VECTOR yy. Used by Single-Variate forecast models such as ARX. that use RECURSIVE multi-horizon forecasting. The first response can't be predicted due to missing past values. Therefore the number of rows in xx and yy is reduced to "y.dim-1" (time 0 cut out).

Value parameters

lags

the maximum lag included (inclusive)

y

the given output/response vector

Attributes

def buildMatrix4TS(y: VectorD, lags: Int, hh: Int): (MatrixD, MatrixD)

Given a response vector y, build and return (1) an input/predictor MATRIX xx and (2) an output/multi-horizon output/response MATRIX yy. Used by Multi-Variate (MV) forecast models such as ARX_MV, that use DIRECT multi-horizon forecasting. The first response can't be predicted as its inputs are only the backcast value. Therefore, the number of rows in xx and yy is reduced to "y.dim-1".

Given a response vector y, build and return (1) an input/predictor MATRIX xx and (2) an output/multi-horizon output/response MATRIX yy. Used by Multi-Variate (MV) forecast models such as ARX_MV, that use DIRECT multi-horizon forecasting. The first response can't be predicted as its inputs are only the backcast value. Therefore, the number of rows in xx and yy is reduced to "y.dim-1".

Value parameters

hh

the maximum forecasting horizon (h = 1, 2, ... hh)

lags

the maximum lag included (inclusive)

y

the given output/response vector, i.e., the time series

Attributes

def buildMatrix4TS_exo(ex: VectorD, lags: Int, elag1: Int, elag2: Int): MatrixD

Given an exogenous variable vector ex corresponding to an endogenous response vector y, build and return an input/predictor MATRIX xx. The first lag responses can't be predicted due to missing past values. Therefore the number of rows in xx is reduced to ex.dim - elag1.

Given an exogenous variable vector ex corresponding to an endogenous response vector y, build and return an input/predictor MATRIX xx. The first lag responses can't be predicted due to missing past values. Therefore the number of rows in xx is reduced to ex.dim - elag1.

Value parameters

elag1

the minimum lag included (inclusive) for the exogenous variable

elag2

the maximum lag included (inclusive) for the exogenous variable

ex

the exogenous variable vector

lags

the maximum lag included (inclusive) for the endogenous variable

Attributes

def buildTensor4TS(y: VectorD, ex: MatrixD, lags: Int, h: Int)(el: Int): (TensorD, MatrixD)

Build a 3D tensor that collects and (lag) expands the endogenous variable and all exogenous variables into the form 'time x lags x variables' --> INPUT TENSOR. Lags for endogenous variable: 1, 2, ... lags Lags for exogenous variables: el, el+1, ... el-1+lags For models like SARIMAX with weekly data, exo variables are not forecasted, so it is not possible to use exogenous lag 1 to make week-two forecasts. Also build a matrix of target values for each forecasting horizon --> OUTPUT MATRIX. The number of rows m = y.dim - el, as forecasts cannot be made unless there is at least one endogenous and one exogenous lag (past value) available to the model. Model: yy_hat = f(xx) with loss function, e.g., || yy - yy_hat ||_F NOTE: for models not taking tensor input, flatten into a matrix.

Build a 3D tensor that collects and (lag) expands the endogenous variable and all exogenous variables into the form 'time x lags x variables' --> INPUT TENSOR. Lags for endogenous variable: 1, 2, ... lags Lags for exogenous variables: el, el+1, ... el-1+lags For models like SARIMAX with weekly data, exo variables are not forecasted, so it is not possible to use exogenous lag 1 to make week-two forecasts. Also build a matrix of target values for each forecasting horizon --> OUTPUT MATRIX. The number of rows m = y.dim - el, as forecasts cannot be made unless there is at least one endogenous and one exogenous lag (past value) available to the model. Model: yy_hat = f(xx) with loss function, e.g., || yy - yy_hat ||_F NOTE: for models not taking tensor input, flatten into a matrix.

Value parameters

el

the first exogenous lag (may be larger than 1)

ex

the exogenous variable matrix over time x exo_vars (e.g., icu_patients, hosp_patient)

h

the forecasting horizon (1, 2, ... h)

lags

the maximum lag included (inclusive)

y

the endogenous variable vector over time (e.g., new_deaths)

Attributes

def buildTensor4TSTest(): Unit

The buildTensor4TSTest main function tests `buildTensor4TS method of the Covid dataset.

The buildTensor4TSTest main function tests `buildTensor4TS method of the Covid dataset.

runMain scalation.modeling.forecasting.buildTensor4TSTest

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 simulated data. Test predictions (one step ahead forecasts).

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

Attributes

See also

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

runMain scalation.modeling.forecasting.nullModelTest

def nullModelTest2(): Unit

The nullModelTest2 main function tests the NullModel class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts).

The nullModelTest2 main function tests the NullModel class on real data: Forecasting lake levels. Test predictions (one step 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 lake levels. Test forecasts (1 to h steps ahead forecasts).

The nullModelTest3 main function tests the NullModel class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts).

Attributes

See also

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

runMain scalation.modeling.forecasting.nullModelTest3

def pq(hpar: HyperParameter): Int
def quadSplineTest(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.quadSplineTest

def quadSplineTest2(): Unit

The quadSplineTest2 main function is used to test the QuadSpline class. Forecasting lake levels.

The quadSplineTest2 main function is used to test the QuadSpline class. Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.quadSplineTest2

def quadSplineTest3(): Unit

The quadSplineTest3 main function tests the QuadSpline class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts).

The quadSplineTest3 main function tests the QuadSpline class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts).

Attributes

See also

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

runMain scalation.modeling.forecasting.quadSplineTest3

def quadSplineTest4(): Unit

The quadSplineTest4 main function is used to test the QuadSpline class. Forecasting Fibonacci numbers.

The quadSplineTest4 main function is used to test the QuadSpline class. Forecasting Fibonacci numbers.

runMain scalation.modeling.forecasting.quadSplineTest4

Attributes

def randomWalkTest(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.aRTest

def randomWalkTest2(): Unit

The randomWalkTest2 main function tests the RandomWalk2 class on real data: Forecasting lake levels. It compares with the other baselines: NullModel and TrendModel. Test predictions (one step ahead forecasts).

The randomWalkTest2 main function tests the RandomWalk2 class on real data: Forecasting lake levels. It compares with the other baselines: NullModel and TrendModel. Test predictions (one step 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 lake levels. Test forecasts (1 to h steps ahead forecasts).

The randomWalkTest3 main function tests the RandomWalk class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts).

Attributes

See also

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

runMain scalation.modeling.forecasting.randomWalkTest3

def randomWalkTest4(): Unit

The randomWalkTest4 main function tests the RandomWalk class on real data: Forecasting COVID-19. Test forecasts (1 to h steps ahead forecasts).

The randomWalkTest4 main function tests the RandomWalk class on real data: Forecasting COVID-19. Test forecasts (1 to h steps ahead forecasts).

runMain scalation.modeling.forecasting.randomWalkTest4

Attributes

def rdot(b: VectorD, y_: VectorD, t: Int): Double

Compute a reverse dot product of the parameter vector b and the most recent actual values in the time series y_, going backwards from y_t. Use max (0, ..) to avoid using negative indices into the y_ vector.

Compute a reverse dot product of the parameter vector b and the most recent actual values in the time series y_, going backwards from y_t. Use max (0, ..) to avoid using negative indices into the y_ vector.

Value parameters

b

the parameter/coefficient vector (e.g., φ for AR)

t

the time point FROM WHICH to make prediction

y_

the actual time series values to use in making predictions

Attributes

def rdot(b: VectorD, yf: MatrixD, r: Int, c: Int): Double

Compute a reverse dot product of the parameter vector b and a diagonal of the the yf matrix starting at element (r, c) and moving up and back. Use max (0, ..) to avoid using negative indices into the yf matrix.

Compute a reverse dot product of the parameter vector b and a diagonal of the the yf matrix starting at element (r, c) and moving up and back. Use max (0, ..) to avoid using negative indices into the yf matrix.

Value parameters

b

the parameter/coefficient vector (e.g., φ for AR)

c

the starting column in the forecast matrix (horizon)

r

the starting row in the forecast matrix (time)

yf

the forecast matrix (time x horizons)

Attributes

def regressionTreeGB4TS2Test(): Unit

The regressionTreeGB4TS2Test main function tests the RegressionTreeGB4TS2 class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The regressionTreeGB4TS2Test main function tests the RegressionTreeGB4TS2 class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.regressionTreeGB4TS2Test

Attributes

def regressionTreeGB4TS2Test2(): Unit

The regressionTreeGB4TS2Test2 main function tests the RegressionTreeGB4TS2 class on real data: Forecasting lake levels.

The regressionTreeGB4TS2Test2 main function tests the RegressionTreeGB4TS2 class on real data: Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.regressionTreeGB4TS2Test2

def regressionTreeGB4TS2Test3(): Unit

The regressionTreeGB4TS2Test3 main function tests the RegressionTreeGB4TS2 class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeGB4TS2Test3 main function tests the RegressionTreeGB4TS2 class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeGB4TS2Test3

Attributes

def regressionTreeGB4TS2Test4(): Unit

The regressionTreeGB4TS2Test4 main function tests the RegressionTreeGB4TS2 class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeGB4TS2Test4 main function tests the RegressionTreeGB4TS2 class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeGB4TS2Test4

Attributes

def regressionTreeGB4TS2Test5(): Unit

The regressionTreeGB4TS2Test5 main function tests the RegressionTreeGB4TS2 class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

The regressionTreeGB4TS2Test5 main function tests the RegressionTreeGB4TS2 class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

runMain scalation.modeling.forecasting.regressionTreeGB4TS2Test5

Attributes

def regressionTreeGB4TS2Test6(): Unit

The regressionTreeGB4TS2Test6 main function tests the RegressionTreeGB4TS2 class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

The regressionTreeGB4TS2Test6 main function tests the RegressionTreeGB4TS2 class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

runMain scalation.modeling.forecasting.regressionTreeGB4TS2Test6

Attributes

def regressionTreeGB4TSTest(): Unit

The regressionTreeGB4TSTest main function tests the RegressionTreeGB4TS class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The regressionTreeGB4TSTest main function tests the RegressionTreeGB4TS class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.regressionTreeGB4TSTest

Attributes

def regressionTreeGB4TSTest2(): Unit

The regressionTreeGB4TSTest2 main function tests the RegressionTreeGB4TS class on real data: Forecasting lake levels.

The regressionTreeGB4TSTest2 main function tests the RegressionTreeGB4TS class on real data: Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.regressionTreeGB4TSTest2

def regressionTreeGB4TSTest3(): Unit

The regressionTreeGB4TSTest3 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeGB4TSTest3 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeGB4TSTest3

Attributes

def regressionTreeGB4TSTest4(): Unit

The regressionTreeGB4TSTest4 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variables. Does Train-n-Test (TnT) Split testing on the model.

The regressionTreeGB4TSTest4 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variables. Does Train-n-Test (TnT) Split testing on the model.

runMain scalation.modeling.forecasting.regressionTreeGB4TSTest4

Attributes

def regressionTreeGB4TSTest5(): Unit

The regressionTreeGB4TSTest5 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeGB4TSTest5 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeGB4TSTest5

Attributes

def regressionTreeGB4TSTest6(): Unit

The regressionTreeGB4TSTest6 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

The regressionTreeGB4TSTest6 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

runMain scalation.modeling.forecasting.regressionTreeGB4TSTest6

Attributes

def regressionTreeGB4TSTest7(): Unit

The regressionTreeGB4TSTest7 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

The regressionTreeGB4TSTest7 main function tests the RegressionTreeGB4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

runMain scalation.modeling.forecasting.regressionTreeGB4TSTest7

Attributes

def regressionTreeMT4TSTest(): Unit

The regressionTreeMT4TSTest main function tests the RegressionTreeMT4TS class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The regressionTreeMT4TSTest main function tests the RegressionTreeMT4TS class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.regressionTreeMT4TSTest

Attributes

def regressionTreeMT4TSTest2(): Unit

The regressionTreeMT4TSTest2 main function tests the RegressionTreeMT4TS class on real data: Forecasting lake levels.

The regressionTreeMT4TSTest2 main function tests the RegressionTreeMT4TS class on real data: Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.regressionTreeMT4TSTest2

def regressionTreeMT4TSTest3(): Unit

The regressionTreeMT4TSTest3 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeMT4TSTest3 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeMT4TSTest3

Attributes

def regressionTreeMT4TSTest4(): Unit

The regressionTreeMT4TSTest4 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variables. Does Train-n-Test (TnT) Split testing on the model.

The regressionTreeMT4TSTest4 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variables. Does Train-n-Test (TnT) Split testing on the model.

runMain scalation.modeling.forecasting.regressionTreeMT4TSTest4

Attributes

def regressionTreeMT4TSTest5(): Unit

The regressionTreeMT4TSTest5 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeMT4TSTest5 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeMT4TSTest5

Attributes

def regressionTreeMT4TSTest6(): Unit

The regressionTreeMT4TSTest6 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

The regressionTreeMT4TSTest6 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

runMain scalation.modeling.forecasting.regressionTreeMT4TSTest6

Attributes

def regressionTreeMT4TSTest7(): Unit

The regressionTreeMT4TSTest7 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

The regressionTreeMT4TSTest7 main function tests the RegressionTreeMT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

runMain scalation.modeling.forecasting.regressionTreeMT4TSTest7

Attributes

def regressionTreeRF4TSTest(): Unit

The regressionTreeRF4TSTest main function tests the RegressionTreeRF4TS class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The regressionTreeRF4TSTest main function tests the RegressionTreeRF4TS class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.regressionTreeRF4TSTest

Attributes

def regressionTreeRF4TSTest2(): Unit

The regressionTreeRF4TSTest2 main function tests the RegressionTreeRF4TS class on real data: Forecasting lake levels.

The regressionTreeRF4TSTest2 main function tests the RegressionTreeRF4TS class on real data: Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.regressionTreeRF4TSTest2

def regressionTreeRF4TSTest3(): Unit

The regressionTreeRF4TSTest3 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeRF4TSTest3 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeRF4TSTest3

Attributes

def regressionTreeRF4TSTest4(): Unit

The regressionTreeRF4TSTest4 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variables. Does Train-n-Test (TnT) Split testing on the model.

The regressionTreeRF4TSTest4 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variables. Does Train-n-Test (TnT) Split testing on the model.

runMain scalation.modeling.forecasting.regressionTreeRF4TSTest4

Attributes

def regressionTreeRF4TSTest5(): Unit

The regressionTreeRF4TSTest5 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeRF4TSTest5 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeRF4TSTest5

Attributes

def regressionTreeRF4TSTest6(): Unit

The regressionTreeRF4TSTest6 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

The regressionTreeRF4TSTest6 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

runMain scalation.modeling.forecasting.regressionTreeRF4TSTest6

Attributes

def regressionTreeRF4TSTest7(): Unit

The regressionTreeRF4TSTest7 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

The regressionTreeRF4TSTest7 main function tests the RegressionTreeRF4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

runMain scalation.modeling.forecasting.regressionTreeRF4TSTest7

Attributes

def regressionTreeRF_MT4TSTest(): Unit

The regressionTreeRF_MT4TSTest main function tests the RegressionTreeRF_MT4TS class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The regressionTreeRF_MT4TSTest main function tests the RegressionTreeRF_MT4TS class. This test is used to CHECK that the buildMatrix4TS function is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.regressionTreeRF_MT4TSTest

Attributes

def regressionTreeRF_MT4TSTest2(): Unit

The regressionTreeRF_MT4TSTest2 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasting lake levels.

The regressionTreeRF_MT4TSTest2 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.regressionTreeRF_MT4TSTest2

def regressionTreeRF_MT4TSTest3(): Unit

The regressionTreeRF_MT4TSTest3 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeRF_MT4TSTest3 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variable only. Does In-Sample Testing (In_ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeRF_MT4TSTest3

Attributes

def regressionTreeRF_MT4TSTest4(): Unit

The regressionTreeRF_MT4TSTest4 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variables. Does Train-n-Test (TnT) Split testing on the model.

The regressionTreeRF_MT4TSTest4 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous variables. Does Train-n-Test (TnT) Split testing on the model.

runMain scalation.modeling.forecasting.regressionTreeRF_MT4TSTest4

Attributes

def regressionTreeRF_MT4TSTest5(): Unit

The regressionTreeRF_MT4TSTest5 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

The regressionTreeRF_MT4TSTest5 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection.

runMain scalation.modeling.forecasting.regressionTreeRF_MT4TSTest5

Attributes

def regressionTreeRF_MT4TSTest6(): Unit

The regressionTreeRF_MT4TSTest6 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

The regressionTreeRF_MT4TSTest6 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model.

runMain scalation.modeling.forecasting.regressionTreeRF_MT4TSTest6

Attributes

def regressionTreeRF_MT4TSTest7(): Unit

The regressionTreeRF_MT4TSTest7 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

The regressionTreeRF_MT4TSTest7 main function tests the RegressionTreeRF_MT4TS class on real data: Forecasts COVID-19 Weekly Data using endogenous and exogenous variables. Does In-Sample Testing (In-ST). Determines the terms to include in the model using Feature Selection. Run Train-n-Test (TnT) Split testing on best model using Rolling Validation.

runMain scalation.modeling.forecasting.regressionTreeRF_MT4TSTest7

Attributes

def rollingValidationTest(): Unit

The rollingValidationTest main function is used to test the rollValidate method in the RollingValidation object.

The rollingValidationTest main function is used to test the rollValidate method in the RollingValidation object.

runMain scalation.modeling.forecasting.rollingValidationTest

Attributes

def rollingValidationTest2(): Unit

The rollingValidationTest2 main function is used to test the rollValidate method in the RollingValidation object.

The rollingValidationTest2 main function is used to test the rollValidate method in the RollingValidation object.

runMain scalation.modeling.forecasting.rollingValidationTest2

Attributes

def rollingValidationTest3(): Unit

The rollingValidationTest3 main function is used to test the rollValidate method in the RollingValidation object. Compares baseline models on in-sample and out-of-sample assessment.

The rollingValidationTest3 main function is used to test the rollValidate method in the RollingValidation object. Compares baseline models on in-sample and out-of-sample assessment.

runMain scalation.modeling.forecasting.rollingValidationTest3

Attributes

def rollingValidationTest4(): Unit

The rollingValidationTest4 main function is used to test the rollValidate method in the RollingValidation object. Random Walk is used to make structure of the yf matrix clear.

The rollingValidationTest4 main function is used to test the rollValidate method in the RollingValidation object. Random Walk is used to make structure of the yf matrix clear.

runMain scalation.modeling.forecasting.rollingValidationTest4

Attributes

def simpleExpSmoothingTest(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.simpleExpSmoothingTest

def simpleExpSmoothingTest2(): Unit

The simpleExpSmoothingTest2 main function is used to test the SimpleExpSmoothing class. Forecasting lake levels. Compare AR(1) and SimpleExpSmoothing models for the time series data.

The simpleExpSmoothingTest2 main function is used to test the SimpleExpSmoothing class. Forecasting lake levels. Compare AR(1) and SimpleExpSmoothing models for the time series data.

Attributes

See also

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

runMain scalation.modeling.forecasting.simpleExpSmoothingTest2

def simpleExpSmoothingTest3(): Unit

The simpleExpSmoothingTest3 main function is used to test the SimpleExpSmoothing class. Test customized smoothing (call smooth) versus optimized smoothing (call train).

The simpleExpSmoothingTest3 main function is used to test the SimpleExpSmoothing class. Test customized smoothing (call smooth) versus optimized smoothing (call train).

runMain scalation.modeling.forecasting.simpleExpSmoothingTest3

Attributes

def simpleExpSmoothingTest4(): Unit

The simpleExpSmoothingTest4 main function is used to test the SimpleExpSmoothing class. Test rolling validation.

The simpleExpSmoothingTest4 main function is used to test the SimpleExpSmoothing class. Test rolling validation.

runMain scalation.modeling.forecasting.simpleExpSmoothingTest4

Attributes

def simpleExpSmoothingTest5(): Unit

The simpleExpSmoothingTest5 main function is used to test the SimpleExpSmoothing class. Forecasting lake levels for several values of the smoothing parameter α.

The simpleExpSmoothingTest5 main function is used to test the SimpleExpSmoothing class. Forecasting lake levels for several values of the smoothing parameter α.

Attributes

See also

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

runMain scalation.modeling.forecasting.simpleExpSmoothingTest5

def simpleMovingAverageTest(): Unit

The simpleMovingAverageTest main function tests the SimpleMovingAverage class on simulated data.

The simpleMovingAverageTest main function tests the SimpleMovingAverage class on simulated data.

runMain scalation.modeling.forecasting.simpleMovingAverageTest

Attributes

def simpleMovingAverageTest2(): Unit

The simpleMovingAverageTest2 main function is used to test the SimpleMovingAverage class.

The simpleMovingAverageTest2 main function is used to test the SimpleMovingAverage class.

runMain scalation.modeling.forecasting.simpleMovingAverageTest2

Attributes

def simpleMovingAverageTest3(): Unit

The simpleMovingAverageTest3 main function is used to test the SimpleMovingAverage class. Forecasting lake levels.

The simpleMovingAverageTest3 main function is used to test the SimpleMovingAverage class. Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.simpleMovingAverageTest3

def simpleMovingAverageTest4(): Unit

The simpleMovingAverageTest4 main function is used to test the SimpleMovingAverage class. Decompose the lake levels dataset.

The simpleMovingAverageTest4 main function is used to test the SimpleMovingAverage class. Decompose the lake levels dataset.

Attributes

See also

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

runMain scalation.modeling.forecasting.simpleMovingAverageTest4

def simpleMovingAverageTest5(): Unit

The simpleMovingAverageTest5 main function is used to test the SimpleMovingAverage class.

The simpleMovingAverageTest5 main function is used to test the SimpleMovingAverage class.

runMain scalation.modeling.forecasting.simpleMovingAverageTest5

Attributes

def ssef(y: VectorD, yp: VectorD, skip: Int): Double

Compute the sum of squares errors assuming the first 'skip' error is zero.

Compute the sum of squares errors assuming the first 'skip' error is zero.

Value parameters

skip

skip this many elements at the beginning (defaults to 1)

y

the actual response vector

yp

the predicted response vector (one-step ahead)

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 testForecast(mod: Fit, y: VectorD, yf: MatrixD, p: Int): MatrixD

Test the actual response vector vs. forecasted matrix, returning the QoF for all forecasting horizons 1 to h. FIX - not agreeing with ForecasterUtil.testHorizons

Test the actual response vector vs. forecasted matrix, returning the QoF for all forecasting horizons 1 to h. FIX - not agreeing with ForecasterUtil.testHorizons

Value parameters

mod

the fittable model (one that extends Fit)

p

the number of variables/lags used in the model

y

the original actual response vector

yf

the forecasted response matrix

Attributes

def trendModelTest(): Unit

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

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

Attributes

See also

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

runMain scalation.modeling.forecasting.trendModelTest

def trendModelTest2(): Unit

The trendModelTest2 main function tests the TrendModel class on real data: Forecasting lake levels. Test predictions (one step ahead forecasts).

The trendModelTest2 main function tests the TrendModel class on real data: Forecasting lake levels. Test predictions (one step 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 lake levels. Test forecasts (1 to h steps ahead forecasts).

The trendModelTest3 main function tests the TrendModel class on real data: Forecasting lake levels. Test forecasts (1 to h steps ahead forecasts).

Attributes

See also

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

runMain scalation.modeling.forecasting.trendModelTest3

def varTest(): Unit

The varTest main function tests the VAR class. This test is used to CHECK that the makeExoCols method (@see apply) is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

The varTest main function tests the VAR class. This test is used to CHECK that the makeExoCols method (@see apply) is working correctly. May get NaN for some maximum lags (p) due to multi-collinearity.

runMain scalation.modeling.forecasting.varTest

Attributes

def varTest2(): Unit

The varTest2 main function tests the VAR class on real data: Forecasting Gas Furnace Data. Performs In-Sample Testing.

The varTest2 main function tests the VAR class on real data: Forecasting Gas Furnace Data. Performs In-Sample Testing.

runMain scalation.modeling.forecasting.varTest2

Attributes

def varTest3(): Unit

The varTest3 main function tests the VAR class on real data: Forecasting COVID-19 Weekly Data. Performs In-Sample Testing. Goal: Find the variable that works best with "new_deaths"

The varTest3 main function tests the VAR class on real data: Forecasting COVID-19 Weekly Data. Performs In-Sample Testing. Goal: Find the variable that works best with "new_deaths"

runMain scalation.modeling.forecasting.varTest3

Attributes

def varTest4(): Unit

The varTest4 main function tests the VAR class on real data: Forecasting COVID-19 Weekly Data. Performs In-Sample Testing. Goal: Find the four variables that works best with "new_deaths"

The varTest4 main function tests the VAR class on real data: Forecasting COVID-19 Weekly Data. Performs In-Sample Testing. Goal: Find the four variables that works best with "new_deaths"

runMain scalation.modeling.forecasting.varTest4

Attributes

def varTest5(): Unit

The varTest5 main function tests the VAR class on real data: Forecasting COVID-19 Weekly Data. Does TnT Testing on endogenous and exogenous variables. Determine the terms to include in the model using Stepwise on In-Sample.

The varTest5 main function tests the VAR class on real data: Forecasting COVID-19 Weekly Data. Does TnT Testing on endogenous and exogenous variables. Determine the terms to include in the model using Stepwise on In-Sample.

runMain scalation.modeling.forecasting.varTest5

Attributes

def varTest6(): Unit

The varTest6 main function tests the VAR class on real data: Forecasting COVID-19 Weekly Data. Does Rolling Validation on variables. Determine the terms to include in the model using Stepwise on In-Sample.

The varTest6 main function tests the VAR class on real data: Forecasting COVID-19 Weekly Data. Does Rolling Validation on variables. Determine the terms to include in the model using Stepwise on In-Sample.

runMain scalation.modeling.forecasting.varTest6

Attributes

def weightedMovingAverageTest(): Unit

The weightedMovingAverageTest main function tests the WeightedMovingAverage class on simulated data.

The weightedMovingAverageTest main function tests the WeightedMovingAverage class on simulated data.

runMain scalation.modeling.forecasting.weightedMovingAverageTest

Attributes

def weightedMovingAverageTest2(): Unit

The weightedMovingAverageTest2 main function is used to test the WeightedMovingAverage class.

The weightedMovingAverageTest2 main function is used to test the WeightedMovingAverage class.

runMain scalation.modeling.forecasting.weightedMovingAverageTest2

Attributes

def weightedMovingAverageTest3(): Unit

The weightedMovingAverageTest3 main function is used to test the WeightedMovingAverage class. Forecasting lake levels.

The weightedMovingAverageTest3 main function is used to test the WeightedMovingAverage class. Forecasting lake levels.

Attributes

See also

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

runMain scalation.modeling.forecasting.weightedMovingAverageTest3

def weightedMovingAverageTest4(): Unit

The weightedMovingAverageTest4 main function is used to test the WeightedMovingAverage class. Decompose the lake levels dataset.

The weightedMovingAverageTest4 main function is used to test the WeightedMovingAverage class. Decompose the lake levels dataset.

Attributes

See also

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

runMain scalation.modeling.forecasting.weightedMovingAverageTest4

def weightedMovingAverageTest5(): Unit

The weightedMovingAverageTest5 main function is used to test the WeightedMovingAverage class.

The weightedMovingAverageTest5 main function is used to test the WeightedMovingAverage class.

runMain scalation.modeling.forecasting.weightedMovingAverageTest5

Attributes

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