TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting

• URL: http://arxiv.org/abs/2501.13041v1
• Date: Wed, 22 Jan 2025 17:40:17 GMT
• Title: TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
• Authors: Yifan Hu, Guibin Zhang, Peiyuan Liu, Disen Lan, Naiqi Li, Dawei Cheng, Tao Dai, Shu-Tao Xia, Shirui Pan,
• Abstract summary: We propose TimeFilter, a graph-based framework for adaptive and fine-grained dependency modeling. TimeFilter filters out irrelevant correlations and preserves the most critical ones through patch-specific filtering.
• Score: 87.71846357354384
• License:
• Abstract: Current time series forecasting methods can be broadly classified into two categories: Channel Independent (CI) and Channel Dependent (CD) strategies, both aiming to capture the complex dependencies within time series data. However, the CI strategy fails to exploit highly correlated covariate information, while the CD strategy integrates all dependencies, including irrelevant or noisy ones, thus compromising generalization. To mitigate these issues, recent works have introduced the Channel Clustering (CC) strategy by grouping channels with similar characteristics and applying different modeling techniques to each cluster. However, coarse-grained clustering cannot flexibly capture complex, time-varying interactions. Addressing the above challenges, we propose TimeFilter, a graph-based framework for adaptive and fine-grained dependency modeling. Specifically, after constructing the graph with the input sequence, TimeFilter filters out irrelevant correlations and preserves the most critical ones through patch-specific filtering. Extensive experiments on 13 real-world datasets from various application domains demonstrate the state-of-the-art performance of TimeFilter. The code is available at https://github.com/TROUBADOUR000/TimeFilter.

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