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- W3127336758 abstract "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." @default.
- W3127336758 created "2021-02-15" @default.
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- W3127336758 date "2020-12-01" @default.
- W3127336758 modified "2023-10-18" @default.
- W3127336758 title "N-BEATS-RNN: deep learning for time series forecasting" @default.
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- W3127336758 doi "https://doi.org/10.1109/icmla51294.2020.00125" @default.
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