用分类路由提升时间序列线性预测，性能媲美调参模型。
CATS-Linear: Classification Auxiliary Linear Model for Time Series Forecasting
- 通过分类器动态分配不同时间序列到专用线性预测器
- 固定超参数下达到领先精度，超越同类固定参数模型
- 适合追求高效稳定预测的工业场景应用
近期研究表明，线性模型可实现与复杂架构相当的时间序列预测性能，但增强线性模型的方法仍不充分。受不同时间序列可能遵循异构线性映射的假设启发，本文提出分类辅助趋势-季节解耦线性模型CATS-Linear，采用分类辅助通道独立性（CACI）机制。CACI通过分类动态将实例路由至专属预测器，实现监督式通道设计。进一步分析了不同通道设置的理论期望风险。此外，重新设计趋势-季节分解结构，引入解耦—线性映射—重耦框架处理趋势分量，以及针对季节分量采用复数域线性投影。大量实验表明，CATS-Linear在固定超参数条件下，性能达到业界最优，与需调参的基线模型相当，并显著优于其他固定超参数方法。
原文摘要 · Abstract (English)
Recent research demonstrates that linear models achieve forecasting performance competitive with complex architectures, yet methodologies for enhancing linear models remain underexplored. Motivated by the hypothesis that distinct time series instances may follow heterogeneous linear mappings, we propose the Classification Auxiliary Trend-Seasonal Decoupling Linear Model CATS-Linear, employing Classification Auxiliary Channel-Independence (CACI). CACI dynamically routes instances to dedicated predictors via classification, enabling supervised channel design. We further analyze the theoretical expected risks of different channel settings. Additionally, we redesign the trend-seasonal decomposition architecture by adding a decoupling -- linear mapping -- recoupling framework for trend components and complex-domain linear projections for seasonal components. Extensive experiments validate that CATS-Linear with fixed hyperparameters achieves state-of-the-art accuracy comparable to hyperparameter-tuned baselines while delivering SOTA accuracy against fixed-hyperparameter counterparts.
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