Channel, Trend and Periodic-Wise Representation Learning for Multivariate Long-term Time Series Forecasting
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915738820804608 |
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| author | Song, Zhangyao Jiang, Nanqing He, Miaohong Zhao, Xiaoyu Guo, Tao |
| author_facet | Song, Zhangyao Jiang, Nanqing He, Miaohong Zhao, Xiaoyu Guo, Tao |
| contents | Downsampling-based methods for time series forecasting have attracted increasing attention due to their superiority in capturing sequence trends. However, this approaches mainly capture dependencies within subsequences but neglect inter-subsequence and inter-channel interactions, which limits forecasting accuracy. To address these limitations, we propose CTPNet, a novel framework that explicitly learns representations from three perspectives: i) inter-channel dependencies, captured by a temporal query-based multi-head attention mechanism; ii) intra-subsequence dependencies, modeled via a Transformer to characterize trend variations; and iii) inter-subsequence dependencies, extracted by reusing the encoder with residual connections to capture global periodic patterns. By jointly integrating these levels, proposed method provides a more holistic representation of temporal dynamics. Extensive experiments demonstrate the superiority of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Channel, Trend and Periodic-Wise Representation Learning for Multivariate Long-term Time Series Forecasting Song, Zhangyao Jiang, Nanqing He, Miaohong Zhao, Xiaoyu Guo, Tao Computational Engineering, Finance, and Science Downsampling-based methods for time series forecasting have attracted increasing attention due to their superiority in capturing sequence trends. However, this approaches mainly capture dependencies within subsequences but neglect inter-subsequence and inter-channel interactions, which limits forecasting accuracy. To address these limitations, we propose CTPNet, a novel framework that explicitly learns representations from three perspectives: i) inter-channel dependencies, captured by a temporal query-based multi-head attention mechanism; ii) intra-subsequence dependencies, modeled via a Transformer to characterize trend variations; and iii) inter-subsequence dependencies, extracted by reusing the encoder with residual connections to capture global periodic patterns. By jointly integrating these levels, proposed method provides a more holistic representation of temporal dynamics. Extensive experiments demonstrate the superiority of the proposed method. |
| title | Channel, Trend and Periodic-Wise Representation Learning for Multivariate Long-term Time Series Forecasting |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.23583 |