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Approaching sales forecasting using recurrent neural networks and transformers

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Approaching sales forecasting using recurrent neural networks and transformers

dc.contributor.author	Vallés Pérez, Iván
dc.contributor.author	Soria Olivas, Emilio
dc.contributor.author	Martínez Sober, Marcelino
dc.contributor.author	Serrano López, Antonio J.
dc.contributor.author	Gómez Sanchís, Juan
dc.contributor.author	Mateo, Fernando
dc.date.accessioned	2023-06-21T10:27:45Z
dc.date.available	2023-06-22T04:45:06Z
dc.date.issued	2022	es_ES
dc.identifier.citation	Vallés-Pérez, I., Soria-Olivas, E., Martínez-Sober, M., Serrano-López, A. J., Gómez-Sanchís, J., & Mateo, F. (2022). Approaching sales forecasting using recurrent neural networks and transformers. Expert Systems with Applications, 201, 116993.	es_ES
dc.identifier.uri	https://hdl.handle.net/10550/88380
dc.description.abstract	Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory placement, network planning, etc.). We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques and the Corporación Favorita data set, published as part of a Kaggle competition. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. Additionally, we describe a training trick for making the model more time independent and hence improving generalization over time. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.	es_ES
dc.language.iso	en	es_ES
dc.publisher	Elsevier	es_ES
dc.subject	sales forecast	es_ES
dc.subject	supply chain	es_ES
dc.subject	deep learning	es_ES
dc.subject	transformer	es_ES
dc.subject	sequence to sequence	es_ES
dc.title	Approaching sales forecasting using recurrent neural networks and transformers	es_ES
dc.type	journal article	es_ES
dc.subject.unesco	UNESCO::CIENCIAS TECNOLÓGICAS	es_ES
dc.identifier.doi	10.1016/j.eswa.2022.116993	es_ES
dc.accrualmethod	CI	es_ES
dc.embargo.terms	0 days	es_ES
dc.type.hasVersion	VoR	es_ES
dc.rights.accessRights	open access	es_ES