arXiv Machine Learning June 5, 2026 By Tajamul Ashraf, Janibul Bashir

FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2408. 11336v3 Announce Type: replace Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns.

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

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

arXiv Machine Learning

Jul 7

GlacierCastAI: Predicting Glacier Retreat from Multi-Modal Satellite Imagery and Climate Signals

arXiv:2607. 04117v1 Announce Type: new Abstract: ERA5 seasonal climate variables contain predictive information about future glacier retreat beyond what satellite imagery alone provides, yet existing deep learning methods focus on mapping current boundaries rather than forecasting future ones.

By Arunkumar Ramachandran

multimodal benchmarks

arXiv AI

Jun 30

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration

arXiv:2604. 16325v3 Announce Type: replace-cross Abstract: Multivariate time series forecasting is fundamental to numerous domains such as energy, finance, and environmental monitoring, where complex temporal dependencies and cross-variable interactions pose enduring challenges.

By Xingsheng Chen, Xianpei Mu, Deyu Yi, Yilin Yuan, Xingwei He, Bo Gao, Regina Zhang, Pietro Lio, Siu-Ming Yiu

llms benchmarks

arXiv Machine Learning

Jul 31

Context-Informed Ship Trajectory Prediction via Conditional Attention

arXiv:2607. 27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation.

By Yuan Guan, Chandler Squires, Timothy Hu, Pradeep Ravikumar

llms agents multimodal safety

arXiv Machine Learning

Jun 9

Observation-driven correction of numerical weather prediction for marine winds

arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.

By Matteo Peduto, Qidong Yang, Jonathan Giezendanner, Devis Tuia, Sherrie Wang

llms rag

arXiv Machine Learning

Jul 3

Extreme Adaptive Transformer for Time Series Forecasting

arXiv:2607. 02437v1 Announce Type: new Abstract: Time series forecasting remains challenging when the underlying data contain rare but critical extreme events.

By Sanjeev Shrestha, Hui Liu, Yifan Zhang

llms benchmarks

Related stories

CITRAS: Covariate-Informed Transformer for Time Series Forecasting

GlacierCastAI: Predicting Glacier Retreat from Multi-Modal Satellite Imagery and Climate Signals

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration

Context-Informed Ship Trajectory Prediction via Conditional Attention

Observation-driven correction of numerical weather prediction for marine winds

Extreme Adaptive Transformer for Time Series Forecasting