时间序列预测新方法，GatedLinear用三个线性基自动选最适合的组合，比大模型更轻量还更准。
GatedLinear提出了一个轻量级框架，利用三个互补线性基（全局趋势季节基、差分增量基、相位对齐递归基）进行自适应路由。它引入Tri-Factorized Fusion Gate，将路由决策分解为通道偏好、水平偏移和相位索引偏差。在标准时间序列基准上，GatedLinear达到或超越了多个复杂基础模型的准确性。该模型参数更少，同时提供可解释的路由模式。
GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting
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.