Abstract
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.
Highlights
Strategies for Improving theIn uncertain environments, decision making based on the analysis of historical data is of utter importance
Within the framework of combining forecasts, our objective in this paper is to develop a procedure to select both forecasting methods and their corresponding weights based on the behavior of well-established models and weighting strategies
We present the main results obtained with the COmbEB algorithm using the forecasting methods introduced in Section 2, and assuming J = 6 and k = 4
Summary
Decision making based on the analysis of historical data is of utter importance. This often implies the implementation and development of different prediction strategies, which have to be adapted to the specific characteristics of the data. The key is to note the importance of deciding on suitable and robust procedures to select the contributed models and their assigned weights so as to produce accurate out-of-sample point forecasts. To this end, this paper proposes a procedure for suitably selecting both methods and weights
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