WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910286971142144 |
|---|---|
| author | Liu, Peiyuan Wu, Beiliang Li, Naiqi Dai, Tao Lei, Fengmao Bao, Jigang Jiang, Yong Xia, Shu-Tao |
| author_facet | Liu, Peiyuan Wu, Beiliang Li, Naiqi Dai, Tao Lei, Fengmao Bao, Jigang Jiang, Yong Xia, Shu-Tao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11319 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting Liu, Peiyuan Wu, Beiliang Li, Naiqi Dai, Tao Lei, Fengmao Bao, Jigang Jiang, Yong Xia, Shu-Tao Machine Learning 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. |
| title | WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2309.11319 |