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Spatio-Temporal Graph Convolutional Networks (STGCN) can tackle the time series prediction problem in traffic domain. Experiments show that STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks.
More detail about STGCN can be found in:
This repository contains a implementation of STGCN based on MindSpore and GraphLearning
This experiment is based on metr-la
GPU:
CUDA_VISIBLE_DEVICES=0 python model_zoo/stgcn/trainval_metr.py --data_path {data_path}
Ascend:
python model_zoo/stgcn/trainval_metr.py --data_path {data_path} --device Ascend
Metr_LA dataset
Test MSE: 0.4
MindSpore Graph Learning is an efficient and easy-to-use graph learning framework, which allows researchers and developers to implement graph models according to formula easily and train efficiently.
https://gitee.com/mindspore/graphlearning
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Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》