Li Li, Yanfei Kang, Fotios Petropoulos, Feng Li
arXiv 18 Apr 2022 · Statistics — Applications · publishedInternational Journal of Production Research (2022) · 22 citations (OpenAlex)
arXiv:2204.08283 · PDF · DOI · OpenAlex · Extracted main text
Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study aims to examine the empirical outcomes of some existing forecast combination methods and propose a generalized feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Makridakis, Spyros, Evangelos Spiliotis, and Vassilios Assimakopoulos (2021) The M5 Competition: Background, Organization, and Implementation | 1.000 | 9 | 4 | 100% |
| 2 | Theodorou, Evangelos, Shengjie Wang, Yanfei Kang, Evangelos Spilioti… (2021) Exploring the Representativeness of the M5 Competition Data self | 1.000 | 8 | 4 | 100% |
| 3 | Kostenko, Audrey V, and Rob J Hyndman (2006) A Note on the Categorization of Demand Patterns | 1.000 | 7 | 3 | 100% |
| 4 | Petropoulos, Fotios, and Nikolaos Kourentzes (2015) Forecast Combinations for Intermittent Demand self | 1.000 | 6 | 4 | 100% |
| 5 | Diebold, Francis X, and Minchul Shin (2019) Machine Learning for Regularized Survey Forecast Combination: Partially-egalitarian LASSO and Its Derivatives | 1.000 | 6 | 3 | 100% |
| 6 | Bates, John M, and Clive WJ Granger (1969) The Combination of Forecasts | 1.000 | 5 | 3 | 100% |
| 7 | Lichtendahl Jr, Kenneth C, and Robert L Winkler (2020) Why Do Some Combinations Perform Better than Others? | 1.000 | 5 | 3 | 100% |
| 8 | Kourentzes, Nikolaos, and George Athanasopoulos (2021) Elucidate Structure in Intermittent Demand Series | 0.928 | 4 | 3 | 100% |
| 9 | Kourentzes, Nikolaos, Devon Barrow, and Fotios Petropoulos (2019) Another Look at Forecast Selection and Combination: Evidence from Forecast Pooling self | 0.874 | 5 | 2 | 100% |
| 10 | Syntetos, Aris A, John E Boylan, and JD Croston (2005) On the Categorization of Demand Patterns | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 73 scored citations.