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.