融合静态与多尺度动态相关性的时序预测新模型
SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting
- 构建双路径图结构，分别学习稳定长期依赖与多尺度动态关联
- 在多个真实数据集上优于现有方法，最高提升达4.2%
- 适合需要捕捉复杂时序依赖的工业、金融场景
准确的多变量时间序列预测依赖于序列间的相关性，这些相关性往往在不同时间尺度上以复杂方式演变。现有方法在建模多尺度依赖方面存在局限，难以捕捉其复杂且动态的特性。为此，本文提出一种新的静态-动态图融合网络（SDGF），核心在于通过双路径图结构学习方法捕捉多尺度序列间相关性。具体而言，模型利用基于先验知识的静态图来锚定长期稳定的依赖关系，同时采用多级小波分解提取多尺度特征，构建自适应学习的动态图以捕捉不同尺度下的关联。设计注意力门控模块智能融合这两种互补信息，并使用多核空洞卷积网络深化对时间模式的理解。在多个广泛使用的现实世界基准数据集上的全面实验验证了所提模型的有效性。代码已开源。
原文摘要 · Abstract (English)
Accurate multivariate time series forecasting hinges on inter-series correlations, which often evolve in complex ways across different temporal scales. Existing methods are limited in modeling these multi-scale dependencies and struggle to capture their intricate and evolving nature. To address this challenge, this paper proposes a novel Static-Dynamic Graph Fusion network (SDGF), whose core lies in capturing multi-scale inter-series correlations through a dual-path graph structure learning approach. Specifically, the model utilizes a static graph based on prior knowledge to anchor long-term, stable dependencies, while concurrently employing Multi-level Wavelet Decomposition to extract multi-scale features for constructing an adaptively learned dynamic graph to capture associations at different scales. We design an attention-gated module to fuse these two complementary sources of information intelligently, and a multi-kernel dilated convolutional network is then used to deepen the understanding of temporal patterns. Comprehensive experiments on multiple widely used real-world benchmark datasets demonstrate the effectiveness of our proposed model. Code is available at https://github.com/shaoxun6033/SDGFNet.
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