Analysis of Weighting Strategies for Improving the Accuracy of Combined Forecasts

This paper deals with the weighted combination of forecasting methods using intelligent strategies for achieving accurate forecasts. In an effort to improve forecasting accuracy, we develop an algorithm that optimizes both the methods used in the combination and the weights assigned to the individual forecasts, COmbEB. The performance of our procedure can be enhanced by analyzing separately seasonal and non-seasonal time series. We study the relationships between prediction errors in the validation set and those of ex-post forecasts for different planning horizons. This study reveals the importance of setting the size of the validation set in a proper way. The performance of the proposed strategy is compared with that of the best prediction strategy in the analysis of each of the 100,000 series included in the M4 Competition.