Elucidate Structure in Intermittent Demand Series

Nikolaos Kourentzes () and George Athanasopoulos ()

No 27/19, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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 of 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.

Keywords: forecasting; temporal aggregation; temporal hierarchies; forecast combination; forecast reconciliation (search for similar items in EconPapers)
Pages: 33
Date: 2019
New Economics Papers: this item is included in nep-ets and nep-for
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Citations: View citations in EconPapers (3)

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Journal Article: Elucidate structure in intermittent demand series (2021)
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