Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings. These architectures take advantage of a graph structure and relational inductive biases to learn a single (global) inductive model to predict any number of the input time series, each associated with a graph node. Despite the gain achieved in computational and data efficiency w.r.t. fitting a set of local models, relying on a single global model can be a limitation whenever some of the time series are generated by a different spatiotemporal stochastic process. The main objective of this paper is to understand the interplay between globality and locality in graph-based spatiotemporal forecasting, while contextually proposing a methodological framework to rationalize the practice of including trainable node embeddings in such architectures. We ascribe to trainable node embeddings the role of amortizing the learning of specialized components. Moreover, embeddings allow for 1) effectively combining the advantages of shared message-passing layers with node-specific parameters and 2) efficiently transferring the learned model to new node sets. Supported by strong empirical evidence, we provide insights and guidelines for specializing graph-based models to the dynamics of each time series and show how this aspect plays a crucial role in obtaining accurate predictions.
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）
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）
The technology of traffic flow forecasting plays an important role in intelligent transportation systems. Based on graph neural networks and attention mechanisms, most previous works utilize the transformer architecture to discover spatiotemporal dependencies and dynamic relationships. However, they have not considered correlation information among spatiotemporal sequences thoroughly. In this paper, based on the maximal information coefficient, we present two elaborate spatiotemporal representations, spatial correlation information (SCorr) and temporal correlation information (TCorr). Using SC…
The University of Edinburgh（爱丁堡大学）；University of Cambridge（剑桥大学）；Central European University（中欧大学）；Obuda University（欧布达大学）；Corvinus University of Budapest（布达佩斯考文纽斯大学）
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing. Newly proposed graph neural network architectures are repetitively evaluated on standard tasks such as traffic or weather forecasting. In this paper, we propose the Chickenpox Cases in Hungary dataset as a new dataset for comparing graph neural network architectures. Our time series analysis and forecasting experiments demonstrate that the Chickenpox Cases in Hungary dataset is adequate for comparing the predictive performance and forecasting capabilities of nove…
STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
SUSTech-UTokyo Joint Research Center on Super Smart City（南方科技大学-东京大学超级智慧城市联合研究中心）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology (SUSTech)（南方科技大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；University of Tokyo（东京大学）
Traffic forecasting is a core element of intelligent traffic monitoring system. Approaches based on graph neural networks have been widely used in this task to effectively capture spatial and temporal dependencies of road networks. However, these approaches can not effectively define the complicated network topology. Besides, their cascade network structures have limitations in transmitting distinct features in the time and space dimensions. In this paper, we propose a Multi-adaptive Spatiotemporal-flow Graph Neural Network (MAF-GNN) for traffic speed forecasting. MAF-GNN introduces an effecti…
University of Virginia（弗吉尼亚大学）；IBM Research（IBM研究院）；UT Austin（德克萨斯大学奥斯汀分校）
Multivariate time series forecasting focuses on predicting future values based on historical context. State-of-the-art sequence-to-sequence models rely on neural attention between timesteps, which allows for temporal learning but fails to consider distinct spatial relationships between variables. In contrast, methods based on graph neural networks explicitly model variable relationships. However, these methods often rely on predefined graphs that cannot change over time and perform separate spatial and temporal updates without establishing direct connections between each variable at every time…
Spatiotemporal forecasting plays an essential role in various applications in intelligent transportation systems (ITS), such as route planning, navigation, and traffic control and management. Deep Spatiotemporal graph neural networks (GNNs), which capture both spatial and temporal patterns, have achieved great success in traffic forecasting applications. Understanding how GNNs-based forecasting work and the vulnerability and robustness of these models becomes critical to real-world applications. For example, if spatiotemporal GNNs are vulnerable in real-world traffic prediction applications, a…
the State Key Laboratory of Complex and Critical Software Environment, Beihang University（北京航空航天大学复杂关键软件环境国家重点实验室）；School of Transportation Science and Engineering, Beihang University（北京航空航天大学交通科学与工程学院）
Leveraging Spatiotemporal Graph Neural Networks for Multi-Store Sales Forecasting
Department of Chemical Engineering, IIT Bombay（印度博亚理工大学化学工程系）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒尔研究实验室）
Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attention
Sorbonne University（索邦大学）；Indian Institute of Science, Education and Research, Kolkata（印度科学教育与研究学院（加尔各答））；International Institute of Information Technology, Bangalore（班加罗尔国际信息技术学院）
Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing
School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算与人工智能学院）；SWJTU-Leeds Joint School, Southwest Jiaotong University（西南交通大学-利兹学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）
Argonne National Laboratory（阿贡国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts without estimates of uncertainty, which are critical for real-time deployments. We focus on a diffusion convolutional recurrent neural network (DCRNN), a state-of-the-art method for short-term traffic forecasting. We develop a scalable deep ensemble approach to quantify uncertainties for DCRNN. Our approach uses a scalable Bayesian optimization method to perform hyperparameter optimization, selects a set of hi…
UiT the Arctic University of Norway（挪威北极圈大学）；NORCE Norwegian Research Centre（挪威研究中心NORCE）；Politecnico di Milano（米兰理工大学）
Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains. Graph neural networks (GNNs) are often the core component of the forecasting architecture. However, in most spatiotemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph, hence hindering the application of these models to large graphs and long temporal sequences. While methods to improve scalability have been proposed in the context of static graphs, few research effort…
A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Models
Imperial College London（帝国理工学院）；Ruhr University Bochum（鲁尔大学波鸿分校）；Queen Mary University of London（伦敦玛丽女王大学）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）
Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks
Rose-Hulman Institute of Technology（罗斯-霍曼理工学院）；Georgia Institute of Technology（佐治亚理工学院）；University of Pennsylvania（宾夕法尼亚大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）
Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting
Frankfurt Institute for Advanced Studies（法兰克福高等研究院）；Goethe Universität Frankfurt（法兰克福大学）；Xidian-FIAS International Joint Research Center（西电-法兰克福高等研究院国际联合研究中心）；School of Science and Engineering, The Chinese University of Hong Kong（香港中文大学（深圳）理工学院）；GSI Helmholtzzentrum für Schwerionenforschung GmbH（GSI亥姆霍兹重离子研究中心）
Robust Traffic Forecasting against Spatial Shift over Years
SUSTech-UTokyo Joint Research Center on Super Smart City（南方科技大学-东京大学超级智慧城市联合研究中心）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology (SUSTech)（南方科技大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Center for Spatial Information Science, University of Tokyo（东京大学空间信息科学中心）
Deep probabilistic forecasting is gaining attention in numerous applications ranging from weather prognosis, through electricity consumption estimation, to autonomous vehicle trajectory prediction. However, existing approaches focus on improvements on the most common scenarios without addressing the performance on rare and difficult cases. In this work, we identify a long tail behavior in the performance of state-of-the-art deep learning methods on probabilistic forecasting. We present two moment-based tailedness measurement concepts to improve performance on the difficult tail examples: Paret…
Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis. Recent research on graph neural networks has made substantial progress in time series forecasting, while little attention has been paid to the kriging problem -- recovering signals for unsampled locations/sensors. Most existing scalable kriging methods (e.g., matrix/tensor completion) are transductive, and thus full retraining is required when we have a new sensor to interpolate. In this paper, we develop an Inductive Graph Neural Network Kriging (IGNNK) model to recover data fo…
To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with higher spatiotemporal frequencies, which in turn requires capturing spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, a method is proposed to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations. The proposed method embeds exogenous factors and captures spatiotemporal correlations using a…
Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting
University of Electronic Science and Technology of China（电子科技大学）；Griffith University（格里菲斯大学）；Peking University（北京大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；The Hong Kong University of Science and Technology（香港科技大学）
Machine learning based traffic forecasting models leverage sophisticated spatiotemporal auto-correlations to provide accurate predictions of city-wide traffic states. However, existing methods assume a reliable and unbiased forecasting environment, which is not always available in the wild. In this work, we investigate the vulnerability of spatiotemporal traffic forecasting models and propose a practical adversarial spatiotemporal attack framework. Specifically, instead of simultaneously attacking all geo-distributed data sources, an iterative gradient-guided node saliency method is proposed t…
Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
CMA Earth system Modeling and Prediction Center (CEMC)（中国气象局地球系统数值预报中心）；State Key Laboratory of Severe Weather (LaSW)（灾害天气国家重点实验室）；School of Computer Science and Engineering, Sichuan University of Science and Engineering（四川轻化工大学计算机科学与工程学院）；School of Electronic and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
Department of Intelligent Systems Engineering, Indiana University Bloomington（印第安纳大学布卢明顿分校智能系统工程系）；Department of Computer Science and Engineering, Texas A&M University（德克萨斯大学阿姆斯特朗分校计算机科学与工程系）
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）
GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models through Statistically-Guided Geo-Prototyping
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Georgia Institute of Technology（佐治亚理工学院）；University of Iowa（爱荷华大学）；Fudan University（复旦大学）；Logistics and Supply Chain MultiTech R&D Centre（物流与供应链多元技术研发中心）
Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting
Heilbrunn Department of Population and Family Health, Columbia University Mailman School of Public Health（哥伦比亚大学流行病学与家庭健康系）；Department of Geography, University of Colorado（科罗拉多大学地理系）；Population Council（人口理事会）
Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecasting
Xinjiang Key Laboratory of Intelligent Computing and Smart Applications（新疆智能计算与智能应用重点实验室）；School of Software, Xinjiang University（新疆大学软件学院）；Department of Computer Science and Technology, Tongji University（同济大学计算机科学与技术系）；Bielefeld University of Applied Sciences and Arts（比勒菲尔德应用科学与艺术大学）
Central South University（中南大学）；Hisense TransTech Co., Ltd.（海信网络科技股份有限公司）；Zhejiang University（浙江大学）
Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding POI distribution. Recently, spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that intro…
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）
UC San Diego（加州大学圣地亚哥分校）；University of Washington（华盛顿大学）；Northeastern University（东北大学）
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. Hence, a systematic study of uncertainty quantification (UQ) methods for spatiotemporal forecasting is missing in the community. In this paper, we describe two types of spatiotemporal forecasting problems: regular grid-based and graph-based. Then we analyz…
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）
Wind power is one of the most important renewable energy sources and accurate wind power forecasting is very significant for reliable and economic power system operation and control strategies. This paper proposes a novel framework with spatiotemporal attention networks (STAN) for wind power forecasting. This model captures spatial correlations among wind farms and temporal dependencies of wind power time series. First of all, we employ a multi-head self-attention mechanism to extract spatial correlations among wind farms. Then, temporal dependencies are captured by the Sequence-to-Sequence (S…
The Hong Kong University of Science and Technology（香港科技大学）
Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Although lots of real-world problems can be viewed as STSF and many research works have proposed machine learning based methods for them, no existing work has summarized and compared these methods from a unified perspective. This survey aims to provide a systematic review of machine learning for STSF. In this survey, we define the STSF problem and classify …
