GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting

• URL: http://arxiv.org/abs/2607.09537v1
• Date: Fri, 10 Jul 2026 15:42:01 GMT
• Title: GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting
• Abstract summary: Time series forecasting requires models to capture diverse, often mutually exclusive, temporal dynamics.<n>Recent deep learning models have improved accuracy, but they typically force these diverse patterns through a single computational backbone.<n>We propose GatedLinear: a lightweight framework that frames forecasting as the adaptive routing of complementary linear bases.
• Score: 11.155508081323694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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