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2024 | OriginalPaper | Buchkapitel

A Proposal for Automatic Demand Forecast Model Selection

verfasst von : Wassim Garred, Raphaël Oger, Anne-Marie Barthe-Delanoe, Matthieu Lauras

Erschienen in: Navigating Unpredictability: Collaborative Networks in Non-linear Worlds

Verlag: Springer Nature Switzerland

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Abstract

Demand forecasting is critical within collaborative networks, enabling suppliers, manufacturers, and retailers to synchronize their operations and achieve enhanced supply chain efficiency. Despite a wealth of research on time series forecast model selection and the availability of numerous forecast models, selecting the most appropriate model for a specific time series remains a challenging task. In this study, an automatic demand forecast model selection procedure is proposed that includes a wide range of statistical and machine learning forecast models. The optimization of the hyperparameters is performed on all the models. The study is validated on M3 monthly data, outperforming all submitted methods and demonstrating significant improvements in terms of accuracy. The approach was also applied to a collaborative network company.

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Titel: A Proposal for Automatic Demand Forecast Model Selection
verfasst von: Wassim Garred
Raphaël Oger
Anne-Marie Barthe-Delanoe
Matthieu Lauras
Verlag: Springer Nature Switzerland
Buch: Navigating Unpredictability: Collaborative Networks in Non-linear Worlds
Print ISBN: 978-3-031-71742-0

Electronic ISBN: 978-3-031-71743-7

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-71743-7_22

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