arXiv Machine Learning

AsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting

AsyncCouple-Flow introduces a new framework for multi‑modal spatio‑temporal forecasting that tackles three key challenges: differing sampling rates, missing modalities, and autoregressive error accumulation. It employs a Modality‑Aware Token Sparsification module to produce equal‑length sequences, an Asynchronous Cross‑Modal Coupling Graph to fuse data under arbitrary asynchrony and missingness, and a Flow‑Matching Forecasting Head that models multi‑step prediction as a conditional ODE. Experiments on weather and traffic datasets demonstrate that the method outperforms state‑of‑the‑art baselines and remains robust even when up to two modalities are missing.

arXiv Machine Learning

Sep 11

Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models

The paper introduces SolCloudLLM, a large language model–based framework that fuses sky‑image patches with time‑series data through bidirectional multimodal fusion for short‑term solar forecasting. Experiments on the SIRTA and SKIPP'D datasets show that SolCloudLLM outperforms existing baselines, achieving up to a 25.4% reduction in mean squared error, especially under cloudy conditions and in few‑shot scenarios.

By Ken Chen, Maneesha Perera, Wei Wang, Sachith Seneviratne, Hansani Weeratunge, Saman Halgamuge

llms rag multimodal

arXiv Machine Learning

Sep 22

UniGIO: Unified Generative Global In-situ Weather Modeling from Spatiotemporal Incomplete Observations

UniGIO is a generative framework that models global in‑situ weather dynamics directly from incomplete GIO data, unifying forecasting, imputation, and generation across arbitrary missing ratios. It employs an Observation Mixer, Event Aligner, Adaptive Temporal Mixer, and a Mixture‑of‑Experts structure to capture station‑level complementarity, temporal dependencies, and extreme events, refining outputs with a Local Refiner. Experiments on the Weather‑5K dataset show state‑of‑the‑art performance, improving accuracy, fidelity, and extreme event capture by 11%, 12%, and 5% respectively.

By Songru Yang, Zili Liu, Tao Han, Ben Fei, Lei Bai, Chang Liu, Zhengxia Zou, Xiangyang Ji, Wanli Ouyang, Zhenwei Shi

benchmarks

arXiv Machine Learning

Jun 3

TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching

arXiv:2606. 03121v1 Announce Type: new Abstract: Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring.

By Zhe Li, Jindong Tian, Hao Miao, Zhi Lei, Chenjuan Guo, Bin Yang

nlp benchmarks

arXiv Machine Learning

Jul 31

Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

arXiv:2607. 28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing.

By Tianen Shen, Zhengyu Li, Yutong Li, Xiangfei Qiu, Xingjian Wu, Bin Yang, Jilin Hu

llms diffusion nlp benchmarks

arXiv AI

Oct 15

WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models

arXiv:2610.14863v1 Announce Type: new Abstract: Data-driven weather forecasting models are commonly trained on ERA5, yet operational deployment must use timely initial states from physics-based numer...

By Mutian Xi, Xinyu Su, Xiaohui Zhong, Hao Li

rag safety

arXiv AI

Aug 24

RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Prediction

RiskTraf introduces a risk-extrapolated residual learning approach for multi-variate traffic flow prediction, leveraging raw flow, speed, and occupancy data from the new PEMSB-3V benchmark. The method freezes a trained spatio-temporal backbone and adds a lightweight residual head that learns from historical speed and occupancy to correct flow predictions across different traffic regimes. Experiments show consistent improvements over various backbones and outperform existing debiasing and distribution-shift adaptation techniques.

By Guangyu Wang, Zhidan Liu

benchmarks

arXiv Machine Learning

Jun 26

Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates

arXiv:2602. 17683v3 Announce Type: replace Abstract: Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture.

By Irene Iele, Giulia Romoli, Daniele Molino, Elena Mulero Ayll\'on, Filippo Ruffini, Paolo Soda, Matteo Tortora

multimodal benchmarks

arXiv Machine Learning

Jun 5

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

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.

By Tajamul Ashraf, Janibul Bashir

llms benchmarks safety

arXiv Machine Learning

Aug 13

FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting

arXiv:2608. 11254v1 Announce Type: new Abstract: Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids.

By Yann Fabel, Bijan Nouri, Milon Miah, Niklas Blum, Luis F. Zarzalejo, Julia Kowalski, Robert Pitz-Paal

diffusion benchmarks

arXiv Machine Learning

Jul 28

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting

arXiv:2605. 05540v2 Announce Type: replace Abstract: Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories.

By Tianyue Yang, Xiao Xue

diffusion benchmarks

arXiv AI

Oct 15

EarthAlign: Learning Language-Aligned Representations of Earth System Data

arXiv:2610.14624v1 Announce Type: cross Abstract: Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive se...

By Fadillah Adamsyah Maani, Numan Saeed, Jiaqi Ma, Dat Tien Nguyen, Youssef Wehbe, Duncan Watson-Parris, Salman Khan

rag nlp multimodal safety

arXiv AI

Jun 18

A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors

arXiv:2606. 19026v1 Announce Type: cross Abstract: Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena.

By David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia, Nick P. Bassill, Chris D. Thorncroft

llms computer-vision safety

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