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A Proposal for Automatic Demand Forecast Model Selection

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Navigating Unpredictability: Collaborative Networks in Non-linear Worlds (PRO-VE 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 727))

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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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Authors and Affiliations

  1. Centre Génie Industriel, IMT Mines Albi, Université de Toulouse, Albi, France

    Wassim Garred, Raphaël Oger, Anne-Marie Barthe-Delanoe & Matthieu Lauras

Authors
  1. Wassim Garred
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  2. Raphaël Oger
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  3. Anne-Marie Barthe-Delanoe
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  4. Matthieu Lauras
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Corresponding author

Correspondence to Wassim Garred .

Editor information

Editors and Affiliations

  1. NOVA University Lisbon, Monte Caparica, Portugal

    Luis M. Camarinha-Matos

  2. Polytechnic University of Valencia, Valencia, Spain

    Angel Ortiz

  3. Mines Saint-Etienne Univ Clermont, Saint-Etienne, France

    Xavier Boucher

  4. IMT Mines Albi, Albi, France

    Anne-Marie Barthe-Delanoë

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Garred, W., Oger, R., Barthe-Delanoe, AM., Lauras, M. (2024). A Proposal for Automatic Demand Forecast Model Selection. In: Camarinha-Matos, L.M., Ortiz, A., Boucher, X., Barthe-Delanoë, AM. (eds) Navigating Unpredictability: Collaborative Networks in Non-linear Worlds. PRO-VE 2024. IFIP Advances in Information and Communication Technology, vol 727. Springer, Cham. https://doi.org/10.1007/978-3-031-71743-7_22

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  • DOI: https://doi.org/10.1007/978-3-031-71743-7_22

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  • Online ISBN: 978-3-031-71743-7

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