Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
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Summary
This paper proposes a general graph neural network framework designed specifically for multivariate time series data that outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.
- Type
- preprint
- Published
- 2020-05-24
- Cited by
- 2,272
- References
- 26
- Access
- Open access
- OpenAlex
- https://openalex.org/W3028192203
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:218869770
Keywords
Multivariate statistics, Computer science, Graph, Exploit, Data mining
References
- Time series analysis, forecasting and control
- Gaussian processes for time-series modelling
- Going deeper with convolutions
- Time series forecasting using a hybrid ARIMA and neural network model
- Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
- Neural Message Passing for Quantum Chemistry
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
- Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting
- Temporal pattern attention for multivariate time series forecasting
- Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- Graph WaveNet for Deep Spatial-Temporal Graph Modeling
- WaveNet: A Generative Model for Raw Audio
- Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
- Structured Sequence Modeling with Graph Convolutional Recurrent Networks
- Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- Semi-Supervised Classification with Graph Convolutional Networks
- DAGCN: Dual Attention Graph Convolutional Networks
- Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
- GMAN: A Graph Multi-Attention Network for Traffic Prediction
Cited by
- Graph Neural Lasso for Dynamic Network Regression
- Graph Convolutional Networks for Graphs Containing Missing Features
- On the inclusion of spatial information for spatio-temporal neural networks
- Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation
- Modeling Complex Spatial Patterns with Temporal Features via Heterogenous Graph Embedding Networks
- Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting
- A General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attention
- Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint
- Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
- Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
- Graph Geometry Interaction Learning
- Graph Neural Networks for Improved El Niño Forecasting
- Cyclic label propagation for graph semi-supervised learning
- Graph Neural Network for Traffic Forecasting: A Survey
- Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure
- Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
- Connecting latent relationships over heterogeneous attributed network for recommendation
- Deep spatial-temporal sequence modeling for multi-step passenger demand prediction
- Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies
- Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT
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