Elucidate structure in intermittent demand series

Nikolaos Kourentzes, George Athanasopoulos

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate for producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.

Original languageEnglish
Pages (from-to)141-152
Number of pages12
JournalEuropean Journal of Operational Research
Volume288
Issue number1
DOIs
Publication statusPublished - Jan 2021

Keywords

  • Forecast combination
  • Forecast reconciliation
  • Forecasting
  • Temporal aggregation
  • Temporal hierarchies

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