Optimal sub-models selection algorithm for combination forecasting model
Optimal sub-models are selected for combination forecasting model. A selection criterion of Max-Linear-Relevance and Min-Linear-Redundancy is proposed. Redundancy removing linear mutual information is measured to improve the accuracy. Monthly time series is forecasted by a novel combination model. The results confirm the applicability and superiority of the combination model.
sub-models selection combination forecasting model max-linear-relevance and min-linear-redundancy monthly time series support vector regression