Abstract
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.
Highlights
The introduction of digital technologies on the industry to provide integration between physical and digital systems has emerged under the form of Industry 4.0 (Frank et al, 2019)
To address the listed problems, this paper proposes and compares one forecasting method selection strategy and two forecasting method combination strategies for demand forecasting problems with different characteristics
This paper proposes the use of two combination strategies for aggregating forecasting methods: simple mean and weighted mean
Summary
The introduction of digital technologies on the industry to provide integration between physical and digital systems has emerged under the form of Industry 4.0 (Frank et al, 2019). The presence of outliers, level shifts, changes in trend, unstable recent trend, functional form, and unusual (last) demands are considered as time series characteristics Another example is the selection based on an information criteria proposed by Qi & Zhang (2001). To address the listed problems, this paper proposes and compares one forecasting method selection strategy and two forecasting method combination strategies for demand forecasting problems with different characteristics (e.g. intermittency, trend, stationary and nonstationary). The main contribution of this work is to propose a set of forecasting models with heterogeneous capabilities, so that the proposed strategies can achieve good results on time series with different data characteristics (such as, intermittency, increasing or decreasing patterns, stationary and nonstationary).
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