Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting

Abstract

Time series forecasting (TSF) is critical in domains like energy, finance, healthcare, and logistics, requiring models that generalize across diverse datasets. Large pre-trained models such as Chronos and Time-MoE show strong zero-shot (ZS) performance but suffer from high computational costs. In this work, we introduce Super-Linear, a lightweight and scalable mixture-of-experts (MoE) model for general forecasting. It replaces deep architectures with simple frequency-specialized linear experts. A lightweight spectral gating mechanism dynamically selects relevant experts, enabling efficient, accurate forecasting. Crucially, resampling during training exposes the model to diverse frequency regimes, while a flexible input adaptation strategy allows it to handle varying inference lengths. Despite its simplicity, Super-Linear demonstrates strong performance across benchmarks, while substantially improving efficiency, robustness to sampling rates, and interpretability.