Predicting short-term PM2.5 concentrations at fine temporal resolutions using a multi-branch temporal graph convolutional neural network

The compressed package (study code.zip) contains the code files implemented by an under review paper ("Predicting short-term PM2.5 concentrations at fine temporal resolutions using a multi-branch temporal graph convolutional neural network").
Among the study code.zip, main.py is the model code based on a multi-branch temporal graph convolutional neural network. tgcn.py is the temporal graph convolutional network. utils.py contains some functions of graph convolution process. input_data.py is data processing.
The zip file (study data.zip) provides an example of air quality data including PM2.5 concentrations and some meteorological data. input_data.zip also contains a N by N adjacency matrix, which describes the spatial relationship between air quality monitoring stations.