用多尺度可变形卷积提升长期时间序列预测精度
MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution
- 设计多尺度时序重排模块，捕捉跨周期特征交互
- 在6个公开数据集上平均提升7.5%，刷新最佳性能
- 适合需要高精度长期预测的工业与金融场景
长期时间序列预测研究主要依赖Transformer和MLP模型，而卷积网络的潜力尚未充分挖掘。为此，本文提出一种新型多尺度时序重排模块，有效捕捉跨周期片段交互与变量依赖关系。在此基础上，构建面向长期预测的多尺度3D可变形卷积框架MS-DFTVNet。为进一步应对时间特征分布不均问题，引入上下文感知的动态可变形卷积机制，增强对复杂时序模式的建模能力。大量实验表明，MS-DFTVNet显著优于强基线模型，在六个公开数据集上平均提升约7.5%，达到新的最优水平。
原文摘要 · Abstract (English)
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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