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scalation/scalation/scalation.modeling/scalation.modeling.forecasting/scalation.modeling.forecasting.reg_trees/RegressionTreeRF_MT4TS

RegressionTreeRF_MT4TS

scalation.modeling.forecasting.reg_trees.RegressionTreeRF_MT4TS
See theRegressionTreeRF_MT4TS companion class
object RegressionTreeRF_MT4TS

The RegressionTreeRF_MT4TS companion object provides factory methods.

Attributes

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

Members list

Value members

Concrete methods

def apply(y: VectorD, lags: Int, h: Int, intercept: Boolean = ..., hparam: HyperParameter = ...): RegressionTreeRF_MT4TS

Create a RegressionTreeRF_MT4TS object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.

Create a RegressionTreeRF_MT4TS object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.

Value parameters

h

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

hparam

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

intercept

whether to add a column of all ones to the matrix (intercept)

lags

the maximum lag included (inclusive)

y

the original un-expanded output/response vector

Attributes

def exo(y: VectorD, lags: Int, ex: MatrixD, h: Int, intercept: Boolean = ..., hparam: HyperParameter = ...): RegressionTreeRF_MT4TS

Create a RegressionTreeRF_MT4TS object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x. In addition, lagged exogenous variables are added.

Create a RegressionTreeRF_MT4TS object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x. In addition, lagged exogenous variables are added.

Value parameters

h

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

hparam

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

intercept

whether to add a column of all ones to the matrix (intercept)

lags

the maximum lag included (inclusive)

y

the original un-expanded output/response vector

Attributes

def rescale(y: VectorD, lags: Int, h: Int, intercept: Boolean = ..., hparam: HyperParameter = ...): RegressionTreeRF_MT4TS

Create a RegressionTreeRF_MT4TS object from a response matrix. The input/data matrix x is formed from the lagged y vectors as columns in matrix x. This method provides data rescaling.

Create a RegressionTreeRF_MT4TS object from a response matrix. The input/data matrix x is formed from the lagged y vectors as columns in matrix x. This method provides data rescaling.

Value parameters

h

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

hparam

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

intercept

whether to add a column of all ones to the matrix (intercept)

lags

the maximum lag included (inclusive)

y

the original un-expanded output/response vector

Attributes

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