N-BEATS-RNN: deep learning for time series forecasting
Ano de defesa: | 2021 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | eng |
Instituição de defesa: |
Universidade Federal de São Carlos
Câmpus Sorocaba |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Ciência da Computação - PPGCC-So
|
Departamento: |
Não Informado pela instituição
|
País: |
Não Informado pela instituição
|
Palavras-chave em Português: | |
Área do conhecimento CNPq: | |
Link de acesso: | https://repositorio.ufscar.br/handle/20.500.14289/13820 |
Resumo: | This work presents N-BEATS-RNN, an extended version of an 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 9 times compared with the original ensembles in N-BEATS. |