arXiv Machine Learning July 28, 2026 By Morad Laglil, Bertrand Pracca, Emilie Devijver, Eric Gaussier

Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2607. 23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never seen during pre-training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 28

Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

arXiv:2602. 17634v2 Announce Type: replace-cross Abstract: Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time series domains.

By Xinghong Fu, Yanhong Li, Georgios Papaioannou, Yoon Kim

llms

arXiv Machine Learning

Jun 29

Unified Zero-Shot Time Series Forecasting: A Darts Foundation

arXiv:2606. 27438v1 Announce Type: new Abstract: Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis.

By Zhihao Dai, Dennis Bader, Alain Gysi

fine-tuning

arXiv Machine Learning

Jun 2

Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting

arXiv:2606. 01289v1 Announce Type: new Abstract: Zero-shot time series forecasting aims to predict future values for previously unseen series, requiring models to generalize temporal dynamics beyond the training distribution.

By Yifan Wu, Junjie Wu, Kai Wu, Xiaoyu Zhang, Jian Lou

llms

arXiv Machine Learning

Jun 4

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

arXiv:2602. 12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.

By Zhongzheng Qiao, Sheng Pan, Anni Wang, Viktoriya Zhukova, Yong Liu, Xudong Jiang, Qingsong Wen, Mingsheng Long, Ming Jin, Chenghao Liu

llms benchmarks

arXiv Machine Learning

Jun 9

Zero and Few Shot Load Forecasting with Large Language Models

arXiv:2411. 11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios.

By Wenlong Liao, Chengrui Zhang, Zhe Yang, Mengshuo Jia, Christian Rehtanz, Jiannong Fang, Fernando Port\'e-Agel

llms nlp fine-tuning

arXiv AI

Aug 7

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting

arXiv:2608. 06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited.

By Yixiong Xiao, Congxi Xiao, Jingbo Zhou

llms rag benchmarks

Related stories

Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

Unified Zero-Shot Time Series Forecasting: A Darts Foundation

Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

Zero and Few Shot Load Forecasting with Large Language Models

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting