{"ID":2869434,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15105","arxiv_id":"2509.15105","title":"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, 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}.","short_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 introduc...","url_abs":"https://arxiv.org/abs/2509.15105","url_pdf":"https://arxiv.org/pdf/2509.15105v3","authors":"[\"Liran Nochumsohn\",\"Raz Marshanski\",\"Hedi Zisling\",\"Omri Azencot\"]","published":"2025-09-18T16:11:31Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":609684,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2869434,"paper_url":"https://arxiv.org/abs/2509.15105","paper_title":"Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting","repo_url":"https://github.com/azencot-group/SuperLinear","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}