Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
Explore this paper's citation graph
Summary
A novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), is proposed, which is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial- Temporal synchronous modeling mechanism.
- Type
- article
- Published
- 2020-04-03
- Cited by
- 1,810
- References
- 26
- Access
- Open access
- OpenAlex
- https://openalex.org/W2996847713
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:214274349
Keywords
Computer science, Temporal database, Graph, Spatial analysis, Data mining
References
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Spectral Networks and Locally Connected Networks on Graphs
- Generating Sequences With Recurrent Neural Networks
- Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
- Freeway Performance Measurement System: Mining Loop Detector Data
- Long Short-Term Memory
- Support Vector Regression Machines
- MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
- On robust estimation of the location parameter
- Time Series Analysis
- Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
- Convolutional Sequence to Sequence Learning
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
- Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting
- Graph Attention Networks
- Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
- Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting
- STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
- Graph WaveNet for Deep Spatial-Temporal Graph Modeling
- Semi-Supervised Classification with Graph Convolutional Networks
Cited by
- A Comprehensive Survey on Traffic Prediction
- Joint Forecasting and Interpolation of Graph Signals Using Deep Learning
- Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
- FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting
- Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction
- A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting
- Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search
- Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
- Graph Neural Networks for Improved El Niño Forecasting
- Multi-stage attention spatial-temporal graph networks for traffic prediction
- Joint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning
- TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting
- GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences
- Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
- Neural Relational Inference with Efficient Message Passing Mechanisms
- Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions
- Graph Neural Network for Traffic Forecasting: A Survey
- Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting
- Pre-Training Time-Aware Location Embeddings from Spatial-Temporal Trajectories
- On prediction of traffic flows in smart cities: a multitask deep learning based approach
Related papers
- Surface State across Scales; Temporal and Spatial Patterns in Land Surface Freeze/Thaw Dynamics
- Scale of inference: on the sensitivity of habitat models for wide‐ranging marine predators to the resolution of environmental data
- Combining continuous spatial and temporal scales for SGD investigations using UAV-based thermal infrared measurements
- A Statistical Method for Detecting Significant Temporal Hotspots Using LISA Statistics
- Combining continuous spatial and temporal scales for SGD investigations using UAV-based thermal infrared measurements
- The role of sampling strategy on apparent temporal stability of soil moisture under subtropical hydroclimatic conditions
- Temporal disaggregation of daily rainfall measurements using regional reanalysis for hydrological applications
- Temporal properties of spatially aggregated meteorological time series