We describe our submission to the Extreme Value Analysis 2019 Data Challenge in which teams were asked to predict extremes of sea surface temperature anomaly within spatio-temporal regions of missing data. We present a computational framework which reconstructs missing data using convolutional deep neural networks. Conditioned on incomplete data, we employ autoencoder-like models as multivariate conditional distributions from which possible reconstructions of the complete dataset are sampled using imputed noise. In order to mitigate bias introduced by any one particular model, a prediction ensemble is constructed to create the final distribution of extremal values. Our method does not rely on expert knowledge in order to accurately reproduce dynamic features of a complex oceanographic system with minimal assumptions. The obtained results promise reusability and generalization to other domains.
STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；School of Aerospace Engineering, Beijing Institute of Technology（航空工程学院，北京理工大学）
LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics
National Key Laboratory of Parallel and Distributed Computing（平行与分布式计算国家重点实验室）；Laboratory of Digitizing Software for Frontier Equipment（前沿设备数字化软件实验室）；College of Computer Science and Technology（计算机科学与技术学院）
PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models
School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Nanyang Technological University（南洋理工大学）
Learning Spatio-Temporal Foundation Models from Pure Synthetic Data
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；National University of Singapore（新加坡国立大学）；Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Polytechnic University（香港理工大学）
Spatiotemporal kriging is an important application in spatiotemporal data analysis, aiming to recover/interpolate signals for unsampled/unobserved locations based on observed signals. The principle challenge for spatiotemporal kriging is how to effectively model and leverage the spatiotemporal dependencies within the data. Recently, graph neural networks (GNNs) have shown great promise for spatiotemporal kriging tasks. However, standard GNNs often require a carefully designed adjacency matrix and specific aggregation functions, which are inflexible for general applications/problems. To address…
Peking University（北京大学）；Hong Kong University of Science and Technology（香港科技大学）
The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches. While STTP can refer to many real-world problems, most existing studies focus on quite specific applications, such as the prediction of taxi demand, ridesharing order, traffic speed, and so on. This hinders the STTP research as the approaches designed for different applications are hardly comparable, and thus how an application-driven approach can be generalized to other scenarios is unclear. T…
Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
School of Advanced Manufacturing Engineering, Nanjing University（南京大学先进制造工程学院）；National Laboratory of Solid State Microstructures, Nanjing University（南京大学固态微结构国家实验室）；Suzhou Acoustics Industry Technology Research Institute Co., Ltd.（苏州声学工业技术研究所有限公司）；School of Mechanical and Electric Engineering, Soochow University（苏州大学机械与电子工程学院）；Shishan Laboratory, Nanjing University（仙山实验室）
SparseST: Exploiting Data Sparsity in Spatiotemporal Modeling and Prediction
Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute（工业与系统工程系，罗切斯特理工学院）；Department of Electrical, Computer, and Systems Engineering and the Department of Computer Science, Rensselaer Polytechnic Institute（电气、计算机与系统工程系和计算机科学系，罗切斯特理工学院）；IBM Research（IBM研究院）
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；ISIR（巴黎索邦大学机器人与智能系统研究所）；MLIA（机器学习与人工智能实验室）；Inria（法国国家信息与自动化研究所）；I3S（信息科学与系统实验室）；Institut Universitaire de France (IUF)（法国大学研究院）；INSA-Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学）；Inserm（法国国家健康与医学研究院）；CREATIS（图像、信号与智能系统研究中心）
Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift
Department of Information Systems, University of Maryland Baltimore County, USA（信息系统系，马里兰大学巴尔的摩县分校）；Department of Earth and Environmental Sciences, KU Leuven, Belgium（地球与环境科学系，鲁汶大学）；Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, USA（大气与海洋科学系，科罗拉多大学博尔德分校）
