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. The implementation of Super-Linear is available at: \href{https://github.com/azencot-group/SuperLinear}{https://github.com/azencot-group/SuperLinear}.

翻译：时间序列预测在能源、金融、医疗和物流等领域至关重要，需要模型能够泛化到不同的数据集。大型预训练模型（如Chronos和Time-MoE）展现出强大的零样本性能，但存在计算成本高的问题。本文提出超线性，一种用于通用预测的轻量级可扩展专家混合模型。它用简单的频率专用线性专家替代了深度架构，这些专家在多个频率域的重采样数据上进行训练。一个轻量级的谱门控机制动态选择相关专家，从而实现高效、准确的预测。尽管结构简单，超线性在多个基准测试中表现出强大的性能，同时显著提升了效率、对采样率的鲁棒性以及可解释性。超线性的实现可在以下网址获取：\href{https://github.com/azencot-group/SuperLinear}{https://github.com/azencot-group/SuperLinear}。