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dc.contributor.authorSbrana, Attilio
dc.contributor.authorDebiaso Rossi, Andre Luis [UNESP]
dc.contributor.authorCoelho Naldi, Murilo
dc.identifier.citationProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, p. 765-768.
dc.description.abstractThis work presents N-BEATS-RNN, an extended version of an existing ensemble of deep learning networks for time series forecasting, N-BEATS. We apply a state-of-the-art Neural Architecture Search, based on a fast and efficient weight-sharing search, to solve for an ideal Recurrent Neural Network architecture to be added to N-BEATS. We evaluated the proposed N-BEATS-RNN architecture in the widely-known M4 competition dataset, which contains 100,000 time series from a variety of sources. N-BEATS-RNN achieves comparable results to N-BEATS and the M4 competition winner while employing solely 108 models, as compared to the original 2,160 models employed by N-BEATS, when composing its final ensemble of forecasts. Thus, N-BEATS-RNN's biggest contribution is in its training time reduction, which is in the order of 9x compared with the original ensembles in N-BEATS.en
dc.relation.ispartofProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
dc.subjectdeep learning
dc.subjectM4 competition
dc.subjectneural architecture search
dc.subjectTime series forecasting
dc.subjectweight sharing
dc.titleN-BEATS-RNN: Deep learning for time series forecastingen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.description.affiliationFederal University of São Carlos Department of Computer Science
dc.description.affiliationSão Paulo State University (UNESP) Campus of Itapeva
dc.description.affiliationUnespSão Paulo State University (UNESP) Campus of Itapeva
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