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STDPatch: A Three-Stream Framework for Long-Term Time Series Forecasting via SG-Filter-Based Decomposition and Patch Refactor
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AI
DOI：10.3390/bdcc10080253.png)
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Abstract
En 中文
Driven by non-stationary factors in real-world sensor-driven applications, time series streams from energy meters, traffic detectors, weather stations, and industrial monitors often exhibit complex patterns composed of long-term trends and multi-scale seasonal fluctuations. Accurately disentangling and modeling these heterogeneous components remains a fundamental challenge in long-term time series forecasting (LTSF). To address this issue, we propose STDPatch, a novel three-stream forecasting framework that combines structural decomposition with architecture specialization. First, we introduce an SG-Filter-Based seasonal–trend decomposition module that employs polynomial fitting to extract shape-preserving trends while reducing seasonal noise. Second, we design a trend decomposition module that further separates the trend component into ascending and descending segments to capture fine-grained evolutionary dynamics. Third, we propose a patch refactor module that adaptively aggregates adjacent patches according to structural similarity, thereby preserving temporal semantic continuity and reducing spurious correlations. Finally, we develop a three-stream architecture that leverages convolutional, linear, and Transformer branches to model seasonal patterns, smooth trends, and non-stationary sub-trends, respectively, with each branch built from efficient, channel-independent components. Extensive experiments on seven real-world sensor-derived benchmark datasets demonstrate that STDPatch consistently outperforms state-of-the-art methods for long-term time series forecasting.
Keywords:
SG-Filter
trend decomposition
patch refactor
three-stream architecture
Journal
B
IF:
4.4
Papers:
1.3K
Citations:
2.4K