Web: http://arxiv.org/abs/2205.08897

Sept. 19, 2022, 1:12 a.m. | Tian Zhou, Ziqing Ma, Xue wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin

cs.LG updates on arXiv.org arxiv.org

Recent studies have shown that deep learning models such as RNNs and
Transformers have brought significant performance gains for long-term
forecasting of time series because they effectively utilize historical
information. We found, however, that there is still great room for improvement
in how to preserve historical information in neural networks while avoiding
overfitting to noise presented in the history. Addressing this allows better
utilization of the capabilities of deep learning models. To this end, we design
a \textbf{F}requency \textbf{i}mproved \textbf{L}egendre …

arxiv film forecasting long-term memory series time series time series forecasting

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