Spatiotemporal information conversion machine for time-series prediction
South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences（中国科学院分子细胞科学卓越创新中心）；Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences（中国科学院动物进化与遗传学卓越创新中心）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；ShanghaiTech University（上海科技大学）
Making predictions in a robust way is a difficult task only based on the observed data of a nonlinear system. In this work, a neural network computing framework, the spatiotemporal information conversion machine (STICM), was developed to efficiently and accurately render a multistep-ahead prediction of a time series by employing a spatial-temporal information (STI) transformation. STICM combines the advantages of both the STI equation and the temporal convolutional network, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the prediction of the target variable. From the observed variables, the STICM also infers the causal factors of the target variable in the sense of Granger causality, which are in turn selected as effective spatial information to improve the prediction robustness of time-series. The STICM was successfully applied to both benchmark systems and real-world datasets, all of which show superior and robust performance in multistep-ahead prediction, even when the data were perturbed by noise. From both theoretical and computational viewpoints, the STICM has great potential in practical applications in artificial intelligence (AI) or as a model-free method based only on the observed data, and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning.
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Southern University of Science and Technology（南方科技大学）；The University of Tokyo（东京大学）；Hong Kong University of Science and Technology（香港科技大学）；Squirrel AI（松鼠AI）；Jilin University（吉林大学）
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
IDSIA USI-SUPSI（IDSIA USI-SUPSI（瑞士意大利语大学与瑞士应用科学大学联合机构））；Università della Svizzera italiana（瑞士意大利语大学）；Politecnico di Milano（米兰理工大学）；UiT the Arctic University of Norway（挪威北极大学）；NORCE, Norwegian Research Centre AS（NORCE挪威研究中心）
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
The Hong Kong University of Science and Technology（香港科技大学）；The Shanghai AI Laboratory（上海人工智能实验室）；University of Cologne（科隆大学）；Qing Yuan Research Institute of Shanghai Jiao Tong University（上海交通大学清源研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
DARTs: A Dual-Path Robust Framework for Anomaly Detection in High-Dimensional Multivariate Time Series
Xi’an Jiaotong University, School of Computer Science and Technology（西安交通大学计算机科学与技术学院）；Fudan University, College of Intelligent Robotics and Advanced Manufacturing（复旦大学智能机器人与先进制造学院）
ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics
Department of Medicinal Chemistry, School of Pharmaceutical Sciences, Fudan University（药学院药物化学系，复旦大学）；School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University（数学科学学院和人工智能学院，上海交通大学）
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP)（NSF HDR极地地区数据与模型变革研究所）；University of North Texas（北得克萨斯大学）
MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
University of Oxford（牛津大学）；Beijing Institute of Technology（北京理工大学）；University of Chicago（芝加哥大学）；State Information Center of China（中国国家信息中心）；North China Institute of Computing Technology（华北计算技术研究所）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanjing University of Information Science and Technology（南京信息工程大学）；Jilin University（吉林大学）；Central South University（中南大学）
ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Bayesian Temporal Factorization for Multidimensional Time Series Prediction
Polytechnique Montreal（蒙特利尔综合理工学院）；McGill University（麦吉尔大学）；Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT)（校际企业网络、物流与交通研究中心（CIRRELT））
Discovering Latent Causal Graphs from Spatiotemporal Data
Department of Computer Science and Engineering, University of California, San Diego, La Jolla, USA（计算机科学与工程系，加州大学圣地亚哥分校，拉乔拉，美国）；Halıcıoglu Data Science Institute, University of California, San Diego, La Jolla, USA（Halıcıoglu数据科学研究所，加州大学圣地亚哥分校，拉乔拉，美国）；Scripps Institution of Oceanography, University of California, San Diego, La Jolla, USA（斯克里普斯海洋研究所，加州大学圣地亚哥分校，拉乔拉，美国）
Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection
College of Computer Science, Zhejiang University（浙江大学计算机科学学院）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Computer Science, Aalborg University（奥胡斯大学计算机科学系）
Spatiotemporal Attention Networks for Wind Power Forecasting
System Engineering Institute, Xi’an Jiaotong University（西安交通大学系统工程研究所）；Rutgers University（罗格斯大学）；Electric Power Research Institute, State Grid Liaoning Electric Power Co. Ltd.（国网辽宁省电力有限公司电力科学研究院）
