The RegressionTreeRF_MT4TS companion object provides factory methods.
Attributes
- Companion
- class
- Graph
-
- Supertypes
-
class Objecttrait Matchableclass Any
- Self type
Members list
Value members
Concrete methods
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
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
- elag1
-
the minimum exo lag included (inclusive)
- elag2
-
the maximum exo lag included (inclusive)
- 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
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