Skip to content

gorgen2020/DVGNN

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 

Repository files navigation

This codes includes our works on Traffic Flow Prediction by Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting.

The code consists of two parts. The first part is dynamic causal graph generation module, and the second part is multi-step prediction module. We supply the demo of transportation and FMRI dataset.

##For the dynamic causal graph generation module, The setting are shown in configuration document:./graph_generation/configurations/Tdrive.conf

Requirements:

  • tensorflow
  • scipy
  • numpy
  • matplotlib
  • pandas
  • math
  • seaborn
  • sklearn
  • argparse
  • configparser
  • time

Run the demo:

./graph_generation/Transportation or FMRI\main.py

Then we will get the parameter file after training, for example, Tdrive_normalization_parameter.npz. Run dynamic_graph_trans_.py to generate the dynamic transition matrix, such as dynamic_Tdrive_adj.npy file.

##For the second part, put the generative file from the first step to ./prediction/Transportation or FMRI/generated_adj (file directory)

Requirements:

  • torch
  • shutil
  • numpy
  • matplotlib
  • pandas
  • math
  • tensorflow
  • sklearn
  • argparse
  • csv
  • time

Run the demo:

./prediction-code/Transportation or FMRI\main.py

If you have any questions, please feel free to email me, gorgen@163.com!

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages