arXiv Machine Learning June 30, 2026 By Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen, Lorenzo Stella, Nick Erickson, Pablo Guerron, Michael Bohlke-Schneider, Yuyang Wang

fev-bench: A Realistic Benchmark for Time Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2509. 26468v3 Announce Type: replace Abstract: Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained models.

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

benchmarks

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 10

CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting

arXiv:2606. 10798v1 Announce Type: new Abstract: Pretrained time series foundation models (TSFMs) have enabled zero-shot forecasting on unseen target series.

By Yosuke Yamaguchi, Issei Suemitsu, Yuki Kajihara, Wenpeng Wei

llms benchmarks

arXiv AI

Jul 21

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.

By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair

benchmarks

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 4

Post-Training Corrections for Improved Time-Series Forecasting

arXiv:2505. 15354v3 Announce Type: replace Abstract: Time-series forecasting is a critical task in various business domains, but it remains inherently challenging.

By Hamza Cherkaoui, Malik Tiomoko, Giuseppe Paolo, Zhang Yili, Yu Meng, Zhang Keli, Hafiz Tiomoko Ali

benchmarks

arXiv AI

Jun 10

CITRAS: Covariate-Informed Transformer for Time Series Forecasting

arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.

By Yosuke Yamaguchi, Issei Suemitsu, Wenpeng Wei

llms

Related stories

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

CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

Unified Zero-Shot Time Series Forecasting: A Darts Foundation

Post-Training Corrections for Improved Time-Series Forecasting

CITRAS: Covariate-Informed Transformer for Time Series Forecasting