Computes the dynamic version of the combined forecast for a method included in the ForecastComb package.
rolling_combine(x, comb_method, criterion = NULL)An object of class 'foreccomb'. Must contrain full training set and test set.
The combination method that should be used.
Specifies loss criterion. Set criterion to either 'RMSE', 'MAE', or 'MAPE' for the methods comb_TA, comb_WA,
comb_EIG3, and comb_EIG4, or to 'NULL' (default) for all other methods.
Returns an object of class foreccomb_res that represents the results for the best-fit forecast combination method:
Returns the best-fit forecast combination method.
Returns the individual input models that were used for the forecast combinations.
Returns the combination weights obtained by applying the best-fit combination method to the training set.
Returns the fitted values of the combination method for the training set.
Returns range of summary measures of the forecast accuracy for the training set.
Returns forecasts produced by the combination method for the test set. Only returned if input included a forecast matrix for the test set.
Returns range of summary measures of the forecast accuracy for the test set. Only returned if input included a forecast matrix and a vector of actual values for the test set.
Returns the data forwarded to the method.
The function rolling_combine allows to estimate a dynamic version of the other combination methods of the package in a
standardized way, i.e., it allows for time-varying weights. The function builds on the idea of time series cross-validation:
Taking the provided training set as starting point, the models are re-estimated at each period of the test set using a
revised (increased) training set.
Like univariate dynamic forecasting, the validation approach requires a full test set -- including the observed values.
The results are stored in an object of class 'foreccomb_res', for which separate plot and summary functions are provided.
Bergmeir, C., Hyndman, R.J., and Koo, B. (2015). A Note on the Validity of Cross-Validation for Evaluating Time Series Prediction. Monash University, Deparment of Econometrics and Business Statistics, Working Paper No. 10/15.
Timmermann, A. (2006). Forecast Combinations. Handbook of Economic Forecasting, 1, 135--196.
obs <- rnorm(100)
preds <- matrix(rnorm(1000, 1), 100, 10)
train_o<-obs[1:80]
train_p<-preds[1:80,]
test_o<-obs[81:100]
test_p<-preds[81:100,]
data<-foreccomb(train_o, train_p, test_o, test_p)
#Static forecast combination (for example OLS):
static_OLS <- comb_OLS(data)
#Dynamic forecast combination:
dyn_OLS <- rolling_combine(data, "comb_OLS")