עומרי אזנקוט

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. The implementation of Super-Linear is publicly available at: https://github.com/azencot-group/SuperLinear.