Comparison of selection and combination strategies for demand forecasting methods

Abstract

Paper aims: In this study, effective strategies to combine and select forecasting methods are proposed. In the selection strategy, the best performing forecasting method from a pool of methods is selected based on its accuracy, whereas the combination strategies are based on the mean methods’ outputs and on the methods’ accuracy. Originality: Despite the large amount of work in this area, the actual literature lacks of selection and combination strategies of forecasting methods for dealing with intermittent time series. Research method: The included forecasting methods are state-of-the-art approaches applied to industrial and academics forecasting problems. Experiments were performed to evaluate the performance of the proposed strategies using a spare part data set of an industry of elevators and a data set from the M3-Competition. Main findings: The results show that, in most cases, the accuracy of the demand forecasts can be improved when using the proposed selection and combination strategies. Implications for theory and practice: The proposed methodology can be applied to forecasting problems, covering a variety of characteristics (e.g., intermittency, trend). The results reveal that combination strategies have potential application, perform better than state-of-the-art models, and have comparable accuracy in intermittent series. Thus, they can be employed to improve production planning activities.