Web: http://arxiv.org/abs/2102.06828

Jan. 31, 2022, 2:11 a.m. | Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang

cs.LG updates on arXiv.org arxiv.org

Recently, deep neural networks have gained increasing popularity in the field
of time series forecasting. A primary reason for their success is their ability
to effectively capture complex temporal dynamics across multiple related time
series. The advantages of these deep forecasters only start to emerge in the
presence of a sufficient amount of data. This poses a challenge for typical
forecasting problems in practice, where there is a limited number of time
series or observations per time series, or both. …

arxiv attention domain adaptation forecasting time time series time series forecasting

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