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.

A comparative study of forecasting methods using real-life econometric series data

“…The work of (Bandeira et al, 2020) proposes a forecasting application strategy considering two procedures: the combination of state-of-the-art forecasting methods and the selection of forecasting methods based on the accuracy of the models. The authors propose two combination strategies: simple mean and weighted mean based on the accuracy of the methods.…”

Measurement of Economic Forecast Accuracy: A Systematic Overview of the Empirical Literature

“…In order to achieve good forecasting accuracy, it is important to use an appropriate forecasting strategy. Research on forecasting strategies have been long in the focus of numerous researchers (Bates and Granger 1969;Makridakis 1988;Bunn 1989;De Menezes et al 2000;Armstrong 2001;Timmermann 2006;Hall and Mitchell 2007;Clark and McCracken 2009;Geweke and Amisano 2011;Kourentzes et al 2014;Fildes and Petropoulos 2015;Nowotarski et al 2016;Pinar et al 2017;Kourentzes et al 2019;Galvão Bandeira et al 2020;Giacalone 2021;Kang et al 2021). In a seminal study on strategies about improving the forecasts accuracy, Bates and Granger (1969) confirmed that combining the forecasts using different models, instead of relying on the individual models, can improve the accuracy of predictions.…”

“…For this purpose, different selection criteria were used. Galvão Galvão Bandeira et al (2020) stated that the selection can be based on the time series characteristics (Petropoulos et al 2018), the forecasting model performance (Wang and Petropoulos 2016;Fildes and Petropoulos 2015), the information criteria (Qi and Zhang 2001), or the judgmental expert selection (Petropoulos et al 2018). Kourentzes et al (2014) proposed a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy.…”

Interpreting direct sales’ demand forecasts using SHAP values

“…Demand forecasting is one of the critical activities of supply chain management (Crum & Palmatier, 2003). As it refers to predicting future sales, demand forecasts support managerial decisions and operational planning throughout the supply chain (Bandeira et al, 2020). For instance, Sales and Operations Planning (S&OP) recognizes demand forecasts as an essential input for the process (Seeling et al, 2019).…”