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arXiv · 2506.17253

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

Chenghan Li·Mingchen Li·Yipu Liao·Ruisheng Diao
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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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Chenghan Li, Mingchen Li, Yipu Liao, Ruisheng Diao. 2025-06-08. MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution. https://arxiv.org/abs/2506.17253

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