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dc.creatorLara Benítez, Pedroes
dc.creatorCarranza García, Manueles
dc.creatorRiquelme Santos, José Cristóbales
dc.identifier.citationLara Benítez, P., Carranza García, M. y Riquelme Santos, J.C. (2021). An Experimental Review on Deep Learning Architectures for Time Series Forecasting. International Journal of Neural Systems, 31 (3)
dc.description.abstractIn recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50,000 time series divided into 12 different forecasting problems. By training more than 38,000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C2es
dc.description.sponsorshipJunta de Andalucía US-1263341es
dc.description.sponsorshipJunta de Andalucía P18-RT-2778es
dc.publisherWorld Scientifices
dc.relation.ispartofInternational Journal of Neural Systems, 31 (3)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.subjectDeep learninges
dc.subjectTime serieses
dc.titleAn Experimental Review on Deep Learning Architectures for Time Series Forecastinges
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.journaltitleInternational Journal of Neural Systemses
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (MICINN). Españaes
dc.contributor.funderJunta de Andalucíaes

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional