To improve forecasting accuracy, researchers employed various combination techniques for a long time. When researchers deal with time series data by using dissimilar models, the combined forecasts of these models are expected to be superior. Deriving a weighting scheme performing better than simple but hard−to−beat combining methods has always been challenging. In this study, a new weighting method based on the hybridisation of combining algorithms is proposed. Five popular datasets were utilised to demonstrate the effectiveness of the proposed method in an out-of-sample context. The results indicate that the proposed method leads to more accurate forecasts than other combining techniques used in the study.
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