arXiv Machine Learning July 10, 2026 By Yongcan Huang, Li Jiang, Ze Yu Liu

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5

Read the original on arXiv Machine Learning →

arXiv:2607. 07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM$_{2.

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

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 10

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

arXiv:2509. 25017v2 Announce Type: replace Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems.

By Spyros Kondylatos, Nikolas Papadopoulos, Gustau Camps-Valls, Ioannis Papoutsis

arXiv Machine Learning

Jul 7

When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters

arXiv:2607. 04919v1 Announce Type: new Abstract: Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost.

By Nicholas Tan Jerome, Frank Simon

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 21

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

arXiv:2607. 17511v1 Announce Type: new Abstract: Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains.

By Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, Yanchang Zhao, Yun Chen

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arXiv Machine Learning

Aug 7

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction

arXiv:2608. 05265v1 Announce Type: new Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.

By Quinn Ledingham, Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring, Saeid Taleghanidoozdoozan, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu

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arXiv AI

Jul 8

The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error

arXiv:2607. 05450v1 Announce Type: cross Abstract: This paper explores the "Granularity Paradox" in time-series forecasting, wherein finer temporal disaggregation (e.

By Hugo Moreira

benchmarks

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

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

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The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error