Feature-based intermittent demand forecast combinations: accuracy and inventory implications

Li Li, Yanfei Kang, Fotios Petropoulos, Feng Li

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)
3 Downloads (Pure)


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 examines the empirical outcomes of some existing forecast combination methods and proposes a generalised 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.

Original languageEnglish
Pages (from-to)7557-7572
JournalInternational Journal of Production Research
Issue number22
Early online date15 Dec 2022
Publication statusPublished - 31 Dec 2023


  • Intermittent demand forecasting
  • diversity
  • empirical evaluation
  • forecast combinations
  • time series features

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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