发表机构
School of Computer Science, Sichuan University（四川大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RouteTS是一种统一时间序列预测框架，通过振幅路由划分频谱并将分量分配至最优计算域，在保留全局周期性与局部变化的同时，兼具预测精度与计算效率优势。
AI 中文摘要
现实世界的时间序列本质上交织着全局周期性结构与局部非平稳变化。现有方法在单一计算域内处理这些异质动态，存在根本局限：时域模型在长预测区间会出现周期性错位，而频域模型会过度平滑瞬态尖峰。我们认为最优计算域并非模型的固有属性，而是由数据本身决定。基于该原则，我们提出RouteTS，这是一种统一的预测框架，通过振幅路由划分频谱并将各分量分配至其数学最优的域：主导频率由频域内的复值线性预测器处理以保留周期性结构，而剩余频谱能量则被还原至时域，由轻量级多层感知机（MLP）建模局部变化。大量实验表明，RouteTS在各类真实世界数据集上均达到了具有竞争力的预测精度，其路由决策由底层频谱特征引导。此外，RouteTS的轻量级设计具备显著的计算效率优势，为解决全局周期性与局部瞬态性这一长期存在的两难问题提供了有原则的解决方案。
英文摘要
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.
