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.
y_t = f(x)
where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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RegressionTreeGB4TS2.type
Members list
Value members
Concrete methods
Align (for the testing set) the actual response vector for comparison with the predicted/forecasted response vector, returning a time vector and sliced response vectors.
Align (for the testing set) the actual response vector for comparison with the predicted/forecasted response vector, returning a time vector and sliced response vectors.
Value parameters
- h_
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the current forecasting horizon - 1
- tr_size
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the size of the intial training set
- y
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the actual response for the full dataset (to be sliced)
- yp
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the predicted response for the full dataset (to be sliced)
Attributes
Create a RegressionTreeGB object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.
Create a RegressionTreeGB 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
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the forecasting horizon (1, 2, ... h)
- hparam
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the hyper-parameters (use RegressionTree.hp for default)
- intercept
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whether to add a column of all ones to the matrix (intercept)
- lags
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the maximum lag included (inclusive)
- y
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the original un-expanded output/response vector
Attributes
Create a RegressionTreeGB 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 RegressionTreeGB 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
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the minimum exo lag included (inclusive)
- elag2
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the maximum exo lag included (inclusive)
- h
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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 RegressionTreeGB 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 RegressionTreeGB 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
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TRAINING SET (tr) and a TESTING SET (te) as follows: [ <-- tr_size --> | <-- te_size --> ] This version calls predict (DIRECT) for h-steps ahead out-of-sample forecasts.
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TRAINING SET (tr) and a TESTING SET (te) as follows: [ <-- tr_size --> | <-- te_size --> ] This version calls predict (DIRECT) for h-steps ahead out-of-sample forecasts.
Value parameters
- mod
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the forecasting model being used (e.g.,
RegressionTreeGB4TS2) - rc
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the retraining cycle (number of forecasts until retraining occurs)
- te_size
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the size of the testing set
Attributes
- See also
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RollingValidation
Split the x matrix and y matrix into training and testing sets.
Split the x matrix and y matrix into training and testing sets.
Value parameters
- ratio
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the ratio of the TESTING set to the full dataset (most common 70-30, 80-20)
- x
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the x data/input matrix
- y
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the y response/output matrix