MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution

• URL: http://arxiv.org/abs/2506.17253v4
• Date: Thu, 02 Oct 2025 01:01:13 GMT
• Title: MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution
• Authors: Chenghan Li, Mingchen Li, Yipu Liao, Ruisheng Diao,
• Abstract summary: We develop MS-DFTVNet, a deformable convolutional framework for long-term forecasting.<n>Experiments demonstrate that MS-DFTVNet not only significantly outperforms strong baselines but also achieves an average improvement of about 7.5%.
• Score: 12.652031472297416
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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