Abstract
Common forecasting methods fail to accurately model the nonlinear and time-varying fluctuations of product demand. Reservoir computing (RC) utilizes a dynamical system to project time-series data to a higher-dimensional state representation extracting mathematical relations within complex demand functions. We demonstrate forecasting accuracy of RC on a multivariate product demand dataset.
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Calvimontes, J., Bürger, J. (2020). Product Demand Forecasting Based on Reservoir Computing. In: Leiras, A., González-Calderón, C., de Brito Junior, I., Villa, S., Yoshizaki, H. (eds) Operations Management for Social Good. POMS 2018. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-23816-2_2
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DOI: https://doi.org/10.1007/978-3-030-23816-2_2
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