Web: http://arxiv.org/abs/2201.12740

June 17, 2022, 1:12 a.m. | Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin

stat.ML updates on arXiv.org arxiv.org

Although Transformer-based methods have significantly improved
state-of-the-art results for long-term series forecasting, they are not only
computationally expensive but more importantly, are unable to capture the
global view of time series (e.g. overall trend). To address these problems, we
propose to combine Transformer with the seasonal-trend decomposition method, in
which the decomposition method captures the global profile of time series while
Transformers capture more detailed structures. To further enhance the
performance of Transformer for long-term prediction, we exploit the fact …

arxiv forecasting lg long-term transformer

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