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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2311.00214 |
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| _version_ | 1866910475890982912 |
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| author | Ou, Wenjie Zhao, Zhishuo Guo, Dongyue Zhang, Zheng Lin, Yi |
| author_facet | Ou, Wenjie Zhao, Zhishuo Guo, Dongyue Zhang, Zheng Lin, Yi |
| contents | Deep learning models have recently achieved significant performance improvements in time series forecasting. We present a highly accurate and simply structured CNN-based model with only one convolutional layer, called WinNet, including (i) Sub-window Division block to transform the series into 2D tensor, (ii) Dual-Forecasting mechanism to capture the short- and long-term variations, (iii) Two-dimensional Hybrid Decomposition (TDD) block to decompose the 2D tensor into the trend and seasonal terms to eliminate the non-stationarity, and (iv) Decomposition Correlation Block (DCB) to leverage the correlation between the trend and seasonal terms by the convolution layer. Results on eight benchmark datasets demonstrate that WinNet can achieve SOTA performance and lower computational complexity over CNN-, MLP- and Transformer-based methods. The code will be available at: https://github.com/ouwen18/WinNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_00214 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | WinNet: Make Only One Convolutional Layer Effective for Time Series Forecasting Ou, Wenjie Zhao, Zhishuo Guo, Dongyue Zhang, Zheng Lin, Yi Machine Learning Deep learning models have recently achieved significant performance improvements in time series forecasting. We present a highly accurate and simply structured CNN-based model with only one convolutional layer, called WinNet, including (i) Sub-window Division block to transform the series into 2D tensor, (ii) Dual-Forecasting mechanism to capture the short- and long-term variations, (iii) Two-dimensional Hybrid Decomposition (TDD) block to decompose the 2D tensor into the trend and seasonal terms to eliminate the non-stationarity, and (iv) Decomposition Correlation Block (DCB) to leverage the correlation between the trend and seasonal terms by the convolution layer. Results on eight benchmark datasets demonstrate that WinNet can achieve SOTA performance and lower computational complexity over CNN-, MLP- and Transformer-based methods. The code will be available at: https://github.com/ouwen18/WinNet. |
| title | WinNet: Make Only One Convolutional Layer Effective for Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2311.00214 |