Forecasting of multivariate time-series is an important problem that has applications in traffic management, cellular network configuration, and quantitative finance. A special case of the problem arises when there is a graph available that captures the relationships between the time-series. In this paper we propose a novel learning architecture that achieves performance competitive with or better than the best existing algorithms, without requiring knowledge of the graph. The key element of our proposed architecture is the learnable fully connected hard graph gating mechanism that enables the use of the state-of-the-art and highly computationally efficient fully connected time-series forecasting architecture in traffic forecasting applications. Experimental results for two public traffic network datasets illustrate the value of our approach, and ablation studies confirm the importance of each element of the architecture. The code is available here: https://github.com/boreshkinai/fc-gaga.
This study applies recurrent neural networks (RNNs), which are known for its ability to process sequential information, to model the spatio-temporal dynamics of land use change (LUC) and to forecast annual land use maps of the city of Tsukuba, Japan. We develop two categories of RNN models: 1) simple RNN, which is the basic RNN variant, 2) three RNN variants with advanced gated architecture: long short-term memory (LSTM), LSTM with peephole connection (LSTM-peephole), and gated recurrent unit (GRU) models. The four models are developed using spatio-temporal data with high temporal resolution, …
Accident detection is a vital part of traffic safety. Many road users suffer from traffic accidents, as well as their consequences such as delay, congestion, air pollution, and so on. In this study, we utilize two advanced deep learning techniques, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), to detect traffic accidents in Chicago. These two techniques are selected because they are known to perform well with sequential data (i.e., time series). The full dataset consists of 241 accident and 6,038 non-accident cases selected from Chicago expressway, and it includes traffic spa…
School of Information Technology, University of Cincinnati（辛辛那提大学信息技术学院）；School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那大学拉法叶分校计算与信息学院）
Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional, long short-term memory (convLSTM) modules. We introduce a novel reduced-gate convolutional LSTM(rgcLSTM) architecture that requires a significantly lower parameter budget than a comparable convLSTM. By using a single multi-function gate, our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, there…
The Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科技大学）；Amazon Web Services（亚马逊网络服务）；Microsoft Research（微软研究院）
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head's importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three real-world datasets show that our GaAN framework achieves stat…
IBM Research – Zurich（IBM研究院苏黎世分部）；Graz University of Technology（格拉茨工业大学）；Institute of Theoretical Computer Science（理论计算机科学研究所）
Biological neural networks are equipped with an inherent capability to continuously adapt through online learning. This aspect remains in stark contrast to learning with error backpropagation through time (BPTT) applied to recurrent neural networks (RNNs), or recently to biologically-inspired spiking neural networks (SNNs). BPTT involves offline computation of the gradients due to the requirement to unroll the network through time. Online learning has recently regained the attention of the research community, focusing either on approaches that approximate BPTT or on biologically-plausible sche…
Central South University（中南大学）；Georgia Institute of Technology（佐治亚理工学院）；Peking University（北京大学）；Zhejiang University（浙江大学）
Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolu…
TRACE: A spatiotemporal contact memory graph network simulator for granular dynamics
Faculty of Engineering and IT, The University of Melbourne（墨尔本大学工程与信息技术学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Norwegian Geotechnical Institute（挪威岩土工程研究所）
GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos
Johns Hopkins University（约翰斯·霍普金斯大学）；Wilmer Eye Institute, Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院威尔默眼科研究所）；Malone Center for Engineering in Healthcare, Johns Hopkins University（约翰斯·霍普金斯大学马龙医疗工程中心）
Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Accurate forecasting of traffic conditions is critical for improving safety, stability, and efficiency of a city transportation system. In reality, it is challenging to produce accurate traffic forecasts due to the complex and dynamic spatiotemporal correlations. Most existing works only consider partial characteristics and features of traffic data, and result in unsatisfactory performances on modeling and forecasting. In this paper, we propose a periodic spatial-temporal deep neural network (PSTN) with three pivotal modules to improve the forecasting performance of traffic conditions through …
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
National University of Singapore（新加坡国立大学）；University of Technology Sydney（悉尼科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Southwest Jiaotong University（西南交通大学）；East China Normal University（华东师范大学）；Aalborg University（奥尔堡大学）；University of Electronic Science and Technology of China（电子科技大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Institute of Structural Engineering, ETH Zurich（苏黎世联邦理工学院结构工程研究所）
Dynamic demand prediction is crucial for the efficient operation and management of urban transportation systems. Extensive research has been conducted on single-mode demand prediction, ignoring the fact that the demands for different transportation modes can be correlated with each other. Despite some recent efforts, existing approaches to multimodal demand prediction are generally not flexible enough to account for multiplex networks with diverse spatial units and heterogeneous spatiotemporal correlations across different modes. To tackle these issues, this study proposes a multi-relational s…
Federal Office of Meteorology and Climatology MeteoSwiss（瑞士联邦气象与气候局）
This paper presents the neural network model that was used by the author in the Weather4cast 2021 Challenge Stage 1, where the objective was to predict the time evolution of satellite-based weather data images. The network is based on an encoder-forecaster architecture making use of gated recurrent units (GRU), residual blocks and a contracting/expanding architecture with shortcuts similar to U-Net. A GRU variant utilizing residual blocks in place of convolutions is also introduced. Example predictions and evaluation metrics for the model are presented. These demonstrate that the model can ret…
Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing. Recently, Graph Neural Network truly outperforms the traditional methods. Nevertheless, the most conventional GNN-based model works well while given a pre-defined graph structure. And the existing methods of defining the graph structures focus purely on spatial dependencies and ignore the temporal correlation. Besides, the semantics of the static pre-defined graph adjacency applied during the whole tra…
Vacant parking space (VPS) prediction is one of the key issues of intelligent parking guidance systems. Accurately predicting VPS information plays a crucial role in intelligent parking guidance systems, which can help drivers find parking space quickly, reducing unnecessary waste of time and excessive environmental pollution. Through the simple analysis of historical data, we found that there not only exists a obvious temporal correlation in each parking lot, but also a clear spatial correlation between different parking lots. In view of this, this paper proposed a graph data-based model ST-G…
StreamMind: Unlocking Full Frame Rate Streaming Video Dialogue through Event-Gated Cognition
University of Science and Technology of China（中国科学技术大学）；Microsoft Research（微软研究院）；Nanjing University（南京大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
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）
ETH Zürich（苏黎世联邦理工学院）；University of Maryland（马里兰大学）；Massachusetts Institute of Technology（麻省理工学院）
We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures. We highlight advantages and limitations of each method and discuss their implementation for parallel computing architectures. We quantify the relative prediction accuracy of these algorithms for the longterm forecasting of chaotic systems using as benchmarks the Lorenz-96 and the Kuramoto-Sivashinsky (KS) equations. We find that, when the ful…
Xiamen University（厦门大学）；University of Melbourne（墨尔本大学）；Zhejiang University（浙江大学）
Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires \emph{inductive learning}. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to pe…
Sharif University of Technology（谢里夫理工大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Georgia Institute of Technology（佐治亚理工学院）；Emory School of Medicine（埃默里大学医学院）
The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert that is time consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generalized automated sleep staging and account for shifts in distributions that may be caused by inherent inter/intra-subject variability, heterogeneity across datasets, and different recording environments. However, these networks ignore the connections among brain regions, and disrega…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Laboratory（中关村实验室）；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）
Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem of privacy leakage aroused by centralized forecasting methods, Federated Learning (FL) has been applied to TFF. However, existing FL-based approaches employ batch learning manner, which makes the pre-trained models inapplicable to subsequent traffic data, thus exhibiting subpar prediction performance. In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL fram…
Hunan University（湖南大学）；State University of New York, New Paltz（纽约州立大学新帕尔茨分校）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）
Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this paper, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station…
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
COSYS-GRETTIA, Gustave Eiffel University, 77420 France（COSYS-GRETTIA，巴黎-伊夫林大学）；ENTPE, University of Lyon（ENTPE，里昂大学）；the LICIT-ECO7 University Gustave Eiffel, France（LICIT-ECO7 巴黎-伊夫林大学）
This paper presents a new approach for predicting team performance from the behavioral traces of a set of agents. This spatiotemporal forecasting problem is very relevant to sports analytics challenges such as coaching and opponent modeling. We demonstrate that our proposed model, Spatial Temporal Graph Convolutional Networks (ST-GCN), outperforms other classification techniques at predicting game score from a short segment of player movement and game features. Our proposed architecture uses a graph convolutional network to capture the spatial relationships between team members and Gated Recur…
Dynamic objects have a significant impact on the robot's perception of the environment which degrades the performance of essential tasks such as localization and mapping. In this work, we address this problem by synthesizing plausible color, texture and geometry in regions occluded by dynamic objects. We propose the novel geometry-aware DynaFill architecture that follows a coarse-to-fine topology and incorporates our gated recurrent feedback mechanism to adaptively fuse information from previous timesteps. We optimize our architecture using adversarial training to synthesize fine realistic tex…
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
École de technologie supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Dibrugarh University（Dibrugarh大学）；Vytautas Magnus University（维陶塔斯马格努斯大学）；University of Southern Queensland（南昆士兰大学）
Localization of Dummy Data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach
Northeast Electric Power University（东北电力大学）；Jilin Electric Power Research Institute of State Grid（国家电网吉林电力科学研究院）；Northeast Electric Power Design and Institute Co., Ltd. of China Power Engineering Consulting Group（中国电力工程顾问集团东北电力设计院有限公司）；The Hong Kong University of Science and Technology（香港科技大学）
Beijing University of Posts and Telecommunications（北京邮电大学）
Traffic forecasting is a problem of intelligent transportation systems (ITS) and crucial for individuals and public agencies. Therefore, researches pay great attention to deal with the complex spatio-temporal dependencies of traffic system for accurate forecasting. However, there are two challenges: 1) Most traffic forecasting studies mainly focus on modeling correlations of neighboring sensors and ignore correlations of remote sensors, e.g., business districts with similar spatio-temporal patterns; 2) Prior methods which use static adjacency matrix in graph convolutional networks (GCNs) are n…
Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Didichuxing Co. Ltd（滴滴出行有限公司）
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions. This paper introduces a new architecture with the graph convolutional networks (GCN) and multivariate Gaussian distributions to perform high-resolution forecasting that applies to any spatiotemporal data. We tackle the sparsity problem in high resolution by leveraging the flexible structure of GCNs and providing a subdivision algorithm. We build our model with Graph Convolutional Gated Recurrent Units (Graph-ConvGRU) to learn spatial, tempora…
