根据数据特性动态选择频域或时域建模，提升时间序列预测精度与效率。
RouteTS: Frequency-Time Routing for Time Series Forecasting
- 按频谱幅值路由，高频成分用频域模型，低频残差用时域轻量模型。
- 在多个真实数据集上达到领先精度，且计算开销显著低于传统方法。
- 适合需要兼顾周期性与瞬时变化的工业级时间序列预测任务。
现实世界的时间序列同时包含全局周期结构与局部非平稳变化。现有方法将这些异质动态统一在单一计算域中处理，存在根本局限：时域模型在长时序上易出现周期错位，频域模型则会过度平滑瞬时尖峰。我们提出，最优计算域并非模型固有属性，而是由数据本身决定。基于此，我们提出 RouteTS，一种统一的预测框架，通过幅值路由划分频谱，并将不同成分分配至其数学最优域。主导频率在频域由复数线性预测器处理以保留周期结构，残余频域能量则转回时域，由轻量 MLP 模拟局部变化。大量实验表明，RouteTS 在多种真实数据集上均实现竞争力预测精度，路由决策由底层频谱特征驱动。此外，其轻量化设计带来显著计算效率优势，为长期存在的全局周期性与局部瞬变之间的权衡问题提供了原则性解决方案。
原文摘要 · Abstract (English)
Real-world time series inherently intertwine global periodic structures with localized non-stationary variations. Existing approaches process these heterogeneous dynamics within a single computational domain, incurring fundamental limitations: time-domain models suffer from periodic misalignment over long horizons, while frequency-domain models over-smooth transient spikes. We argue that the optimal computational domain is not a property of the model, but of the data itself. Based on this principle, we propose RouteTS, a unified forecasting framework that partitions the frequency spectrum via amplitude routing and delegates components to their mathematically optimal domains. Dominant frequencies are processed by a complex-valued linear predictor in the frequency domain to preserve periodic structure, while residual spectral energy is reverted to the time domain and modeled by a lightweight MLP for local variations. Extensive experiments demonstrate that RouteTS achieves competitive prediction accuracy across diverse real-world datasets, with routing decisions guided by the underlying spectral signature. Furthermore, the lightweight design of RouteTS provides significant computational efficiency advantages, offering a principled solution to the longstanding dilemma between global periodicity and local transience.
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