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

scalation.modeling.forecasting.reg_trees

package scalation.modeling.forecasting.reg_trees

Members list

Type members

Classlikes

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
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 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

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 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 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

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 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

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
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

Attributes

Companion
object
Supertypes
trait ForecasterX
trait Fit
trait FitM
trait Predictor
trait Model
class Object
trait Matchable
class Any
Show all
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
final class regressionTreeGB4TSTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest6

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeGB4TSTest7

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest6

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeMT4TSTest7

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest6

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF4TSTest7

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest4

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest5

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest6

Attributes

Supertypes
class Object
trait Matchable
class Any
final class regressionTreeRF_MT4TSTest7

Attributes

Supertypes
class Object
trait Matchable
class Any
final class rollingValidationTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class rollingValidationTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class rollingValidationTest4

Attributes

Supertypes
class Object
trait Matchable
class Any

Value members

Concrete methods

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 forecast models 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". FIX - utilize MakeMatrix4TS

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 forecast models 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". FIX - utilize MakeMatrix4TS

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 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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.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.reg_trees.rollingValidationTest2

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.reg_trees.rollingValidationTest4

Attributes

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