WFTNet: Exploiting Global and Local Periodicity in Long-Term Time Series Forecasting
Peiyuan Liu, Beiliang Wu, Naiqi Li, Tao Dai, Fengmao Lei, Jigang Bao, Yong Jiang, Shu-Tao Xia
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.
BibTeX
@inproceedings{icassp2024_wftnetexploiting,
title = {WFTNet: Exploiting Global and Local Periodicity in Long-Term Time Series Forecasting},
author = {Peiyuan Liu and Beiliang Wu and Naiqi Li and Tao Dai and Fengmao Lei and Jigang Bao and Yong Jiang and Shu-Tao Xia},
booktitle = {ICASSP 2024},
year = {2024}
}