Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；INTR Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）INTR部）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）
Junfeng Hu, Yuxuan Liang, Zhencheng Fan, Li Liu, Yifang Yin, Roger Zimmermann
Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision-making. However, it is impractical to deploy massive sensors due to the expensive costs, resulting in sparse data collection. Therefore, how to get fine-grained data measurement has long been a pressing issue. In this paper, we aim to infer values at non-sensor locations based on observations from available sensors (termed spatiotemporal inference), where capturing spatiotemporal relationships among the data plays a critical role. Our investigations reveal two significant insights that have not been explored by previous works. Firstly, data exhibits distinct patterns at both long- and short-term temporal scales, which should be analyzed separately. Secondly, short-term patterns contain more delicate relations including those across spatial and temporal dimensions simultaneously, while long-term patterns involve high-level temporal trends. Based on these observations, we propose to decouple the modeling of short-term and long-term patterns. Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns. Furthermore, we propose a graph recurrent network with a time skip strategy to alleviate the gradient vanishing problem and model the long-term dependencies. Experimental results on four public real-world datasets demonstrate that our method effectively captures both long- and short-term relations, achieving state-of-the-art performance against existing methods.
Spatiotemporal Graph Neural Networks in short term load forecasting: Does adding Graph Structure in Consumption Data Improve Predictions?
Interdisciplinary Centre for Security, Reliability and Trust - SnT, University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心 - SnT）；University of Luxembourg（卢森堡大学）
Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging
Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Technische Universität Berlin（柏林工业大学）；Korea University（高丽大学）；Max Planck Institut für Informatik（马克斯·普朗克信息学研究所）；Basque Center on Cognition, Brain and Language（巴斯克认知、大脑与语言中心）；Ikerbasque（伊克尔巴斯克基金会）
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking
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）
Mitigating Backdoors via Decoy Shortcuts and Knowledge Decoupling
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
School of Engineering and Technology, China University of Geosciences (Beijing)（工程与技术学院，中国地质大学（北京））；School of Geological Engineering and Geomatics, Chang'an University（地质工程与测绘学院，长安大学）
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）
