Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917521284661248 |
|---|---|
| author | Nochumsohn, Liran Marshanski, Raz Zisling, Hedi Azencot, Omri |
| author_facet | Nochumsohn, Liran Marshanski, Raz Zisling, Hedi Azencot, Omri |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting Nochumsohn, Liran Marshanski, Raz Zisling, Hedi Azencot, Omri Machine Learning 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}. |
| title | Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.15105 |