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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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.

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Jun 10

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

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Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting

The paper critiques the prevalent use of mean squared error (MSE) for evaluating irregular time‑series forecasting, arguing that MSE is biased by timestamp sampling distributions. It introduces the Continuous‑time Squared Error (CSE), an importance‑weighted metric that theoretically offers a tighter asymptotic bound on continuous‑time risk than MSE. A comprehensive benchmark across synthetic, semi‑synthetic, and eight real‑world datasets demonstrates that CSE more accurately recovers continuous‑time risk, revealing limitations of relying solely on MSE.

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benchmarks

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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

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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

The Nixtlaverse: An Open-Source Ecosystem for Forecasting

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