发表机构
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对时间序列预测中多样动态难以用单一机制处理的问题，提出门控线性框架，通过三种专门机制及三因式融合门实现互补线性基的自适应路由，实验显示其在精度、可解释性和参数规模上表现出色。
AI 中文摘要
时间序列预测要求模型捕捉多样且往往相互排斥的时间动态，从平滑趋势延续到非平稳漂移和严格相位对齐的循环。近期深度学习模型虽提高了准确性，但通常通过单一计算主干处理这些多样模式。我们提出门控线性框架，将预测视为互补线性基的自适应路由。它利用三种机制：用于平滑投影的全局趋势 - 季节基、用于非平稳漂移的基于差分的增量基和用于显式循环重用的相位对齐循环基。通过三因式融合门动态协调，在不同预测模式下进行高度粒度化的逐点软路由。实验表明该方法取得了与近期复杂基础模型相当或更优的精度，且路由模式可解释、参数规模小。
英文摘要
Time series forecasting requires models to capture diverse, often mutually exclusive, temporal dynamics, from smooth trend continuation to nonstationary drift and strict phase-aligned recurrence. While recent deep learning models have improved accuracy, they typically force these diverse patterns through a single computational backbone governed by fixed algorithmic inductive biases (e.g., self-attention or spectral filtering). This single-mechanism approach often struggles with the profound heterogeneity of real-world series, where different variables and forecast horizons necessitate fundamentally different predictive treatments. To address this, we propose GatedLinear: a lightweight framework that frames forecasting as the adaptive routing of complementary linear bases. GatedLinear leverages a pool of three specialized mechanisms: a global trend-seasonal basis for smooth projection, a difference-based incremental basis for nonstationary drift, and a phase-aligned recurrence basis for explicit cyclic reuse. To dynamically orchestrate these distinct behaviors, we introduce a Tri-Factorized Fusion Gate that disentangles routing decisions into channel-specific preferences, horizon-aware offsets, and phase-indexed biases derived from known future time marks. This design allows the model to perform highly granular, point-wise soft routing across different predictive regimes without stacking computationally heavy neural modules. Experiments on standard benchmarks show that our method achieves state-of-the-art or highly competitive accuracy against recent complex foundational models, while offering explicitly interpretable routing patterns and operating with a substantially smaller parameter footprint.
