Skip to content

Latest commit

 

History

History
81 lines (66 loc) · 3.6 KB

README.md

File metadata and controls

81 lines (66 loc) · 3.6 KB
Raw

Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention

Introduction

We propose a new dynamic multi-graph fusion module to characterize the correlations of nodes within a graph and the nodes across graphs via the spatial attention and graph attention mechanisms. Furthermore, we introduce a trainable weight tensor to indicate the importance of each node in different graphs. We can increase the performance of plenty of STGCNN models including STGCN, ASTGCN, MSTGCN, ST-MGCN, and Graph WaveNet.

Framework

Datasets

Histocical Records

  • Air Quality The Ministry of Ecology and Environment of China (MEE) published a large-scale air quality dataset, comprising 92 air quality monitoring stations, to assess the hourly PM2.5 concentration in Jiangsu province in 2020.

Weight Matrix

There are five weight matrices:

  • Distance Graph:
  • Neighbor Graph:
  • Functionality graph:
  • Heuristic Graph:
  • Temporal Pattern Similarity Graph:

Multi-graph Spatial Embedding

The multi-graph spatial embedding is generated using node2vec.

The historical records are stored in 'data\temporal_data', the weight matrix data is stores in 'data\graph', and the multi-graph spatial embedding data is stores in 'data\SE'.

Requirements

Our code is based on Python3 (>= 3.6). The major libraries are listed as follows:

  • torch >= 1.8.1
  • numpy >= 1.15
  • scipy >= 1.1.0
  • torch-cluster >= 1.5.9
  • torch-geometric >= 1.7.2
  • torch-scatter >= 2.0.6
  • torch-sparse >= 0.6.9
  • torch-spline-conv >= 1.2.1
  • pytorch-lightning >= 1.2.8
  • wandb >= 0.11.1

The following command can help install the above libraries:

pip install -r requirements.txt

Usage

  1. Generate training data The time steps of historical observations and prediction horizons are both set to 24. The train, validation, and test part are divided into 7:1:2. Run the following command to generate training data:
python generate_training_data.py
  1. Parameters
  • graphs: distance graph, neighbor graph, functionality graph, heuristic graph, temporal pattern similarity graph
  • model: ASTGCN
  • length of histotical observations: 24
  • length of prediction horizons: 24
  • epoches: 40
  • batch size: 32
  • learning rate: 1e-4
  • weight decay: 1e-4
  • number of attention heads: 8
  • dimension of each attention outputs: 8
  1. Train the model Run the following command to train model:
python train.py

Citation

Please refer to our paper. Wei Shao*, Zhiling Jin*, Shuo Wang, et al, Flora Salim, Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention. Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention. In Proceedings of the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022), 2022.

@article{shao2022longterm,
  title={Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention},
  author={Shao, Wei and Jin, Zhiling and Wang, Shuo and Kang, Yufan and Xiao, Xiao and Menouar, Hamid and Zhang, Zhaofeng and Zhang, Junshan and Salim, Flora},
  journal={arXiv preprint arXiv:2204.11008},
  year={2022}
}