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Artificial intelligence in supply chain management: investigation of transfer learning to improve demand forecasting of intermittent time series with deep learning

  • Demand forecasting intermittent time series is a challenging business problem. Companies have difficulties in forecasting this particular form of demand pattern. On the one hand, it is characterized by many non-demand periods and therefore classical statistical forecasting algorithms, such as ARIMA, only work to a limited extent. On the other hand, companies often cannot meet the requirements for good forecasting models, such as providing sufficient training data. The recent major advances of artificial intelligence in applications are largely based on transfer learning. In this paper, we investigate whether this method, originating from computer vision, can improve the forecasting quality of intermittent demand time series using deep learning models. Our empirical results show that, in total, transfer learning can reduce the mean square error by 65 percent. We also show that especially short (65 percent reduction) and medium long (91 percent reduction) time series benefit from this approach.

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Metadaten
Author of HS ReutlingenKiefer, Daniel; Grimm, Florian; van Dinther, Clemens
URN:urn:nbn:de:bsz:rt2-opus4-38719
URL:http://hdl.handle.net/10125/79537
ISBN:978-0-9981331-5-7
Erschienen in:Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS 2022), 4-7 January 2022, virtual event/Maui
Publisher:University of Hawai'i at Manoa
Place of publication:Honolulu
Document Type:Conference proceeding
Language:English
Publication year:2022
Tag:artificial intelligence; deep learning; demand forecasting; intermittent time series; transfer learning
Page Number:10
First Page:1656
Last Page:1665
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