N-BEATS-RNN: Deep learning for time series forecasting
Nenhuma Miniatura disponível
Data
2020-12-01
Autores
Sbrana, Attilio
Debiaso Rossi, Andre Luis [UNESP]
Coelho Naldi, Murilo
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
This 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
Palavras-chave
deep learning, M4 competition, neural architecture search, Time series forecasting, weight sharing
Como citar
Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, p. 765-768.