WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting

• URL: http://arxiv.org/abs/2309.11319v2
• Date: Thu, 4 Jan 2024 06:41:05 GMT
• Title: WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
• Authors: Peiyuan Liu, Beiliang Wu, Naiqi Li, Tao Dai, Fengmao Lei, Jigang Bao, Yong Jiang, Shu-Tao Xia
• Abstract summary: We propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. Tests on various time series datasets show WFTNet consistently outperforms other state-of-the-art baselines.
• Score: 61.64303388738395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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