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Demand forecasting intermittent and lumpy time series: comparing statistical, machine learning and deep learning methods

  • Forecasting intermittent and lumpy demand is challenging. Demand occurs only sporadically and, when it does, it can vary considerably. Forecast errors are costly, resulting in obsolescent stock or unmet demand. Methods from statistics, machine learning and deep learning have been used to predict such demand patterns. Traditional accuracy metrics are often employed to evaluate the forecasts, however these come with major drawbacks such as not taking horizontal and vertical shifts over the forecasting horizon into account, or indeed stock-keeping or opportunity costs. This results in a disadvantageous selection of methods in the context of intermittent and lumpy demand forecasts. In our study, we compare methods from statistics, machine learning and deep learning by applying a novel metric called Stock-keeping-oriented Prediction Error Costs (SPEC), which overcomes the drawbacks associated with traditional metrics. Taking the SPEC metric into account, the Croston algorithm achieves the best result, just ahead of a Long Short-Term Memory Neural Network.

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Metadaten
Author of HS ReutlingenKiefer, Daniel; Grimm, Florian; van Dinther, Clemens
URN:urn:nbn:de:bsz:rt2-opus4-34969
DOI:https://doi.org/10.24251/HICSS.2021.172
ISBN:978-0-9981331-4-0
Erschienen in:Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS-54), 4-8 January 2021, online
Publisher:University of Hawai'i at Manoa
Place of publication:Honolulu
Document Type:Conference proceeding
Language:English
Publication year:2021
Tag:Intelligent Decision Support for Logistics and Supply Chain Management; deep learning; demand forecasting; intermittent; lumpy; spec
Page Number:10
First Page:1425
Last Page:1434
DDC classes:004 Informatik
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International