Awesome-GNN4TS

<div align="center">   <h2><b> Awesome Graph Neural Networks for Time Series Analysis (GNN4TS) </b></h2> </div> <div align="center">

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[<a href="https://arxiv.org/abs/2307.03759">Paper Page</a>] [<a href="https://mp.weixin.qq.com/s/_G2WieJPrWcaK8aegXObUA">中文解读1</a>] [<a href="https://mp.weixin.qq.com/s/ZsSj6C_uJd2dqmynXcrOSA">中文解读2</a>] [<a href="https://zhuanlan.zhihu.com/p/643249754">中文解读3</a>] [<a href="https://mp.weixin.qq.com/s?__biz=Mzk0NDE5Nzg1Ng==&mid=2247507893&idx=1&sn=99ef8465c09cbcd3346d2d4019f7b3b5&chksm=c32ac63af45d4f2c1141d31923252ca6bbff123564c9424d452f046ab98854a3219dbd08d01d#rd">中文解读4</a>]

</div> <p align="center"> <img src="./assets/gnn4ts.png" width="350"> </p>

🔥 Abundant resources related to GNNs for time series analysis (GNN4TS) by Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan

🙋 Please let us know if you find out a mistake or have any suggestions!

🌟 If you find this resource helpful, please consider to star this repository and cite our survey paper:

@article{jin2024gnn4ts,
  title={A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection},
  author={Jin, Ming and Koh, Huan Yee and Wen, Qingsong and Zambon, Daniele and Alippi, Cesare and Webb, Geoffrey I and King, Irwin and Pan, Shirui},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year={2024}
}

Time series analysis is a fundamental task in many real-world applications, such as finance, healthcare, and transportation. Recently, graph neural networks (GNNs) have been widely used in time series analysis. This repository aims to collect the resources related to GNNs for time series analysis (GNN4TS).

时间序列分析是许多现实应用场景中的一项基本任务，例如对金融、医疗、和交通运输数据的分析与建模。近年来，图神经网络（GNN）已广泛应用于时间序列分析。本项目旨在收集整理与时间序列分析相关图神经网络（GNN4TS）的资源。

We provide two taxonomies for GNN4TS. The first taxonomy (left) is task-oriented and the second taxonomy (right) is model-oriented. The task-oriented taxonomy is based on the tasks that GNNs are used for in time series analysis. The model-oriented taxonomy is based on the types of GNNs used in time series analysis.

针对GNN4TS的大框架，我们提出了两种分类法：其一（左）是面向任务的，其次（右）是面向模型的。第一种分类法基于GNN在时间序列分析中施展的具体任务进行划分，第二种分类法则基于时间序列分析中GNN的类型与设计进行归纳。

✨ News

[2024-08-09] 🔥 Our survey was accepted by IEEE TPAMI (IF 20.8). 🎉
[2023-08-09] 📮 Our updated version (ver. 10 Aug) of the survey is released [paper link]
[2023-07-07] 📮 Our GNN4TS survey (ver. 11 Jul) is made available on arXiv ~~[paper link]~~
[2023-06-19] 📮 We have released this repository that collects the resources related to GNNs for time series analysis (GNN4TS). We will keep updating this repository, and welcome to STAR🌟 and WATCH to keep track of it.

🔭 Table of Contents

Awesome-GNN4TS

📚 Collection of Papers

GNNs for Time Series Forecasting (GNN4TSF)

Diffusion convolutional recurrent neural network: Data-driven traffic forecasting (ICLR, 2018) [paper]
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting (IJCAI, 2018) [paper]
Urban traffic prediction from spatio-temporal data using deep meta learning (KDD, 2019) [paper]
Autoregressive Models for Sequences of Graphs (IEEE IJCNN, 2019) [paper]
ST-UNet: A Spatio-Temporal U-Network forGraph-structured Time Series Modeling (arXiv, 2019) [paper]
Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting (AAAI, 2019) [paper]
Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting (MileTS, 2019) [paper]
Attention Based Spatial-Temporal Graph Convolutional Networksfor Traffic Flow Forecasting (AAAI, 2019) [paper]
Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting (AAAI, 2019) [paper]
Graph wavenet for deep spatial-temporal graph modeling (IJCAI, 2019) [paper]
STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting (IJCAI, 2019) [paper]
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting (AAAI, 2020) [paper]
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (KDD, 2020) [paper]
Traffic Flow Prediction via Spatial Temporal Graph Neural Network (WWW, 2020) [paper]
Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems (WWW, 2020) [paper]
GMAN: A Graph Multi-Attention Network for Traffic Prediction (AAAI, 2020) [paper]
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting (AAAI, 2020) [paper]
Spatio-Temporal Graph Structure Learning for Traffic Forecasting (AAAI, 2020) [paper]
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting (NeurIPS, 2020) [paper]
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (NeurIPS, 2020) [paper]
GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification (IJCAI, 2020) [paper]
LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks (IJCAI, 2020) [paper]
ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed (CIKM, 2020) [paper]
Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting (ICDM, 2020) [paper]
Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data (ECAI, 2020) [paper]
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction (ECCV, 2020) [paper]
Discrete Graph Structure Learning for Forecasting Multiple Time Series (ICLR, 2021) [paper]
MTHetGNN: A heterogeneous graph embedding framework for multivariate time series forecasting (Pattern Recognition, 2021) [paper]
Graph Edit Networks (ICLR, 2021) [paper]
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting (ICML, 2021) [paper]
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting (KDD, 2021) [paper]
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting (AAAI, 2021) [paper]
Hierarchical Graph Convolution Network for Traffic Forecasting (AAAI, 2021) [paper]
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network (AAAI, 2021) [paper]
TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning (IJCAI, 2021) [paper]
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting (ICML, 2022) [paper]
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks (NeurIPS, 2022) [paper]
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities (CIKM, 2022) [paper]
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs (IEEE TKDE, 2022) [paper]
Graph Neural Controlled Differential Equations for Traffic Forecasting (AAAI, 2022) [paper]
CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting (AAAI, 2022) [paper]
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search (ACM TKDD, 2022) [paper]
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting (ICLR, 2022) [paper]
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting (KDD, 2022) [paper]
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting (KDD, 2022) [paper]
Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning (KDD, 2022) [paper]
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting (IJCAI, 2022) [paper]
Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention (IJCAI, 2022) [paper]
FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffc Flow Forecasting (IJCAI, 2022) [paper]
METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting (VLDB, 2022) [paper]
Scalable Spatiotemporal Graph Neural Networks (AAAI, 2023) [paper]
Graph State-Space Models (arXiv,