stackridge2f.RdThis function performs a two-stage stacked forecast using ridge regression. In the first stage, it generates base forecasts from the training portion of the time series. In the second stage, these forecasts are used as external regressors to produce the final forecast on the testing set.
stackridge2f(y, h = 5L, split_fraction = 0.5, ...)A multivariate time series object.
Integer. Forecast horizon for the second-stage (final) forecast. Defaults to 5.
Numeric between 0 and 1. Fraction of the time series used for training. The rest is used for testing and generating stacking features. Defaults to 0.5.
Additional arguments passed to ahead::ridge2f in both
stages (e.g., lags, lambda_1, lambda_2, nb_hidden, etc.).
An object returned by ahead::ridge2f for the stacked forecast,
typically a list including mean and prediction intervals.
The function works as follows:
Split the time series into training and testing sets according to
split_fraction.
Generate base forecasts from the training set using ahead::ridge2f
Use these base forecasts as external regressors (xreg) to predict
the testing set with a second ahead::ridge2f model.
This approach allows stacking of forecasts to potentially improve accuracy by leveraging the predictions of multiple first-stage models.
For multivariate base learners (e.g., tslm with multiple dependent
variables), the function automatically extracts forecasts from each series
and combines them into the feature matrix.
if (FALSE) { # \dontrun{
# Univariate example with default ridge2f base learner
result1 <- stackridge2f(fpp2::insurance, h = 10,
split_fraction = 0.5)
} # }