SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908787781140480 |
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| author | Wang, Shaoxun Zhang, Xingjun Li, Qianyang Cao, Jiawei Tan, Zhendong |
| author_facet | Wang, Shaoxun Zhang, Xingjun Li, Qianyang Cao, Jiawei Tan, Zhendong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18135 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting Wang, Shaoxun Zhang, Xingjun Li, Qianyang Cao, Jiawei Tan, Zhendong Machine Learning Artificial Intelligence 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. |
| title | SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.18135 |