Continuous dynamical combination of short and long-term forecasts for nonstationary time series

This brief generalizes the forecasting method that has been awarded first-place winner in the International Competition of Time Series Forecasting (ICTSF 2012). It is based on a short-term forecasting approach of multilayer perceptrons (MLP) ensembles, combined dynamically with a long-term forecasting. The main feature of this general approach is the original concept of continuous dynamical combination of forecasts, in which the weights of the forecasting combination are a function of forecast horizon. Experiments in ICTSFs and NN5s nonstationary time series show that this new combination method improves the performance in multistep forecasting of MLP ensembles when compared to the MLP ensembles alone.