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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.17253 |
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| _version_ | 1866908572594470912 |
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| author | Li, Chenghan Li, Mingchen Liao, Yipu Diao, Ruisheng |
| author_facet | Li, Chenghan Li, Mingchen Liao, Yipu Diao, Ruisheng |
| contents | Research on long-term time series prediction has primarily relied on Transformer and MLP models, while the potential of convolutional networks in this domain remains underexplored. To address this, we propose a novel multi-scale time series reshape module that effectively captures cross-period patch interactions and variable dependencies. Building on this, we develop MS-DFTVNet, the multi-scale 3D deformable convolutional framework tailored for long-term forecasting. Moreover, to handle the inherently uneven distribution of temporal features, we introduce a context-aware dynamic deformable convolution mechanism, which further enhances the model's ability to capture complex temporal patterns. Extensive experiments demonstrate that MS-DFTVNet not only significantly outperforms strong baselines but also achieves an average improvement of about 7.5% across six public datasets, setting new state-of-the-art results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17253 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution Li, Chenghan Li, Mingchen Liao, Yipu Diao, Ruisheng Machine Learning Artificial Intelligence Research on long-term time series prediction has primarily relied on Transformer and MLP models, while the potential of convolutional networks in this domain remains underexplored. To address this, we propose a novel multi-scale time series reshape module that effectively captures cross-period patch interactions and variable dependencies. Building on this, we develop MS-DFTVNet, the multi-scale 3D deformable convolutional framework tailored for long-term forecasting. Moreover, to handle the inherently uneven distribution of temporal features, we introduce a context-aware dynamic deformable convolution mechanism, which further enhances the model's ability to capture complex temporal patterns. Extensive experiments demonstrate that MS-DFTVNet not only significantly outperforms strong baselines but also achieves an average improvement of about 7.5% across six public datasets, setting new state-of-the-art results. |
| title | MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.17253 |