FCTA: A Forecasting Combined Methodology with a Threshold Accepting Approach

Abstract

The combination of forecasting methods is a widespread technique. The most common technique for ensembling several individual methods is scoring. These ensemble methods have been useful for designing hybrid forecasting time series methods in several areas. However, more precise applications are required in modern times, and hybridizations using several ranking approaches have emerged to solve this problem. The main difficulty of this technique is finding the most suitable methodology to combine forecasting methods. This work presents a new methodology named FCTA (forecasting combined method with threshold accepting) for ensembling several forecasting methods. This methodology uses a Threshold Accepting algorithm for weighting individual predictions. FCTA starts from an initial weighting and aims to find the best ponderation of the individual methods by optimizing the precision of the global prediction. For testing FCTA, we selected a dataset taken from M4-Makridakis-competition, and we compared it with the best individual forecasting methods. FCTA is also compared with other successful methodologies. The experimentation shows that FCTA surpasses the best M4 individual methods and is equivalent or better than the best methodologies of the area.