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2024 | OriginalPaper | Chapter

A Proposal for Automatic Demand Forecast Model Selection

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

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

Publisher: 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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Literature
1.
go back to reference Abdallah, M., Rossi, R., Mahadik, K., Kim, S., Zhao, H., Bagchi, S.: AutoForecast: Automatic time-series forecasting model selection. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. pp. 5–14. CIKM’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3511808.3557241 Abdallah, M., Rossi, R., Mahadik, K., Kim, S., Zhao, H., Bagchi, S.: AutoForecast: Automatic time-series forecasting model selection. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. pp. 5–14. CIKM’22, Association for Computing Machinery, New York, NY, USA (2022). https://​doi.​org/​10.​1145/​3511808.​3557241
4.
go back to reference Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A nextgeneration hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A nextgeneration hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
7.
go back to reference Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for Hyper-Parameter Optimization. In: Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc. (2011) Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for Hyper-Parameter Optimization. In: Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc. (2011)
12.
go back to reference Fildes, R.A.: Beyond forecasting competitions. Int. J. Forecast. 17(4), 556–560 (2001) Fildes, R.A.: Beyond forecasting competitions. Int. J. Forecast. 17(4), 556–560 (2001)
19.
go back to reference Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)CrossRef Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)CrossRef
31.
go back to reference Pegels, C.C.: Exponential forecasting: some new variations. Manage. Sci. 15(5), 311–315 (1969) Pegels, C.C.: Exponential forecasting: some new variations. Manage. Sci. 15(5), 311–315 (1969)
Metadata
Title
A Proposal for Automatic Demand Forecast Model Selection
Authors
Wassim Garred
Raphaël Oger
Anne-Marie Barthe-Delanoe
Matthieu Lauras
Copyright Year
2024
DOI
https://doi.org/10.1007/978-3-031-71743-7_22

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