N-BEATS-RNN: Deep learning for time series forecasting

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorSbrana, Attilio-
Autor(es): dc.creatorDebiaso Rossi, Andre Luis [UNESP]-
Autor(es): dc.creatorCoelho Naldi, Murilo-
Data de aceite: dc.date.accessioned2022-02-22T00:50:17Z-
Data de disponibilização: dc.date.available2022-02-22T00:50:17Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICMLA51294.2020.00125-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/207451-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/207451-
Descrição: dc.descriptionThis 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.-
Descrição: dc.descriptionFederal University of São Carlos Department of Computer Science-
Descrição: dc.descriptionSão Paulo State University (UNESP) Campus of Itapeva-
Descrição: dc.descriptionSão Paulo State University (UNESP) Campus of Itapeva-
Formato: dc.format765-768-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020-
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Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectM4 competition-
Palavras-chave: dc.subjectneural architecture search-
Palavras-chave: dc.subjectTime series forecasting-
Palavras-chave: dc.subjectweight sharing-
Título: dc.titleN-BEATS-RNN: Deep learning for time series forecasting-
Aparece nas coleções:Repositório Institucional - Unesp

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