1 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models
Siru Zhong, Junjie Qiu, Yangyu Wu +7
Spatio-Temporal (ST) Foundation Models (STFMs) promise cross-dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of do…
Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review
Yuchen Fang, Hao Miao, Yuxuan Liang +9
Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction. However, previous deep learning models designed for specific tas…
From Time Series Analysis to Question Answering: A Survey in the LLM Era
Wei Li, Zhe Xie, Yuxuan Liang +4
Recently, Large Language Models (LLMs) have introduced a novel paradigm in Time Series Analysis (TSA), leveraging strong language capabilities to support tasks such as forecasting…
Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems
Jindong Tian, Yuxuan Liang, Ronghui Xu +6
Air pollution significantly threatens human health and ecosystems, necessitating effective air quality prediction to inform public policy. Traditional approaches are generally cate…