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A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

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A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

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Lorente-Leyva, LL.; Alemany Díaz, MDM.; Peluffo-Ordóñez, DH.; Herrera-Granda, ID. (2021). A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry. Lecture Notes in Computer Science. 131-142. https://doi.org/10.1007/978-3-030-64580-9_11

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Metadatos del ítem

Título: A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry
Autor: Lorente-Leyva, Leandro L. Alemany Díaz, María Del Mar Peluffo-Ordóñez, Diego H. Herrera-Granda, Israel D.
Entidad UPV: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Fecha difusión:
Resumen:
[EN] This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present ...[+]
Palabras clave: Demand forecasting methods , Textile industry , Machine learning , Classical methods , Forecast error
Derechos de uso: Reserva de todos los derechos
Fuente:
Lecture Notes in Computer Science. (issn: 0302-9743 )
DOI: 10.1007/978-3-030-64580-9_11
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/978-3-030-64580-9_11
Título del congreso: 6th International Conference on Machine Learning, Optimization, and Data Science (LOD 2020)
Lugar del congreso: Siena, Italy
Fecha congreso: Julio 19-23,2020
Serie: Lecture Notes in Computer Science
Agradecimientos:
The authors are greatly grateful by the support given by the SDAS Research Group (https://sdas-group.com/).
Tipo: Artículo Comunicación en congreso Capítulo de libro

References

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