Web: http://arxiv.org/abs/2109.09705

Jan. 31, 2022, 2:11 a.m. | Philippe Chatigny, Shengrui Wang, Jean-Marc Patenaude, Boris N. Oreshkin

cs.LG updates on arXiv.org arxiv.org

We study the problem of efficiently scaling ensemble-based deep neural
networks for multi-step time series (TS) forecasting on a large set of time
series. Current state-of-the-art deep ensemble models have high memory and
computational requirements, hampering their use to forecast millions of TS in
practical scenarios. We propose N-BEATS(P), a global parallel variant of the
N-BEATS model designed to allow simultaneous training of multiple univariate TS
forecasting models. Our model addresses the practical limitations of related
models, reducing the training …

arxiv forecasting neural scale

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