arXiv AI June 24, 2026 By Zijiang Yan, Yixiang Huang, Jianhua Pei, Hina Tabassum, Luca Chiaraviglio

EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting

arXiv:2512. 15067v4 Announce Type: replace-cross Abstract: The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to ensure ongoing compliance, assess potential health impacts, and support efficient network planning.

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

diffusion

arXiv Machine Learning

Jun 3

A Systematic Evaluation of Current Architectures in Wind Power Forecasting

arXiv:2606. 02849v1 Announce Type: new Abstract: Interval wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it accounts for the inherent uncertainty of wind resources.

By Vinicius Bortolini, Gilson Adamczuk Oliveira, Erick Oliveira Rodrigues, Matheus Henrique Dal Molin Ribeiro

arXiv Machine Learning

Jul 15

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents

arXiv:2511. 23276v2 Announce Type: replace Abstract: Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports.

By Joongwon Chae, Runming Wang, Chen Xiong, Gong Yunhan, Lian Zhang, Ji Jiansong, Dongmei Yu, Peiwu Qin

llms agents

arXiv Machine Learning

Jun 19

Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting

arXiv:2606. 19560v1 Announce Type: new Abstract: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need.

By Alireza Jafari, Judy Fox, Geoffrey C. Fox, Madhav Marathe, Aniruddha Adiga

llms

arXiv AI

Jul 15

Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.

By Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok Jeon

llms

arXiv Machine Learning

Jun 30

Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion

arXiv:2606. 28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures.

By Ruchi Pakhle

llms rag benchmarks

arXiv AI

Jul 14

Multi-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series

arXiv:2607. 10740v1 Announce Type: cross Abstract: The analysis of Multivariate Time Series (MTS) plays an important role in a lot of real-world practical applications, but it still remains some challenging problem about capturing multi-granularity structural patterns and suppressing noise appropriately.

By HaoChong Fu, Jian Xu

rag benchmarks

Related stories

A Systematic Evaluation of Current Architectures in Wind Power Forecasting

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents

Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting

Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion

Multi-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series