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

TMLR Paper7196 Authors

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, trained on resampled data across
multiple frequency regimes. A lightweight spectral gating mechanism dynamically selects
relevant experts, enabling efficient, accurate forecasting. Despite its simplicity, Super-Linear
demonstrates strong performance across benchmarks, while substantially improving efficiency,
robustness to sampling rates, and interpretability.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Jacek_Cyranka1

Submission Number: 7196