  Recent CNN and Transformer-based models tried to utilize frequency and
periodicity information for long-term time series forecasting. However, most
existing work is based on Fourier transform, which cannot capture fine-grained
and local frequency structure. In this paper, we propose a Wavelet-Fourier
Transform Network (WFTNet) for long-term time series forecasting. WFTNet
utilizes both Fourier and wavelet transforms to extract comprehensive
temporal-frequency information from the signal, where Fourier transform
captures the global periodic patterns and wavelet transform captures the local
ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to
adaptively balance the importance of global and local frequency patterns.
Extensive experiments on various time series datasets show that WFTNet
consistently outperforms other state-of-the-art baseline. Code is available at
https://github.com/Hank0626/WFTNet.
