Multivariate Time Series Forecasting Model with Graph Neural Networks Incorporating Multi-scale Local and Global Information Fusion
by Guan-Hua Chen, Kai-Lung Hua
Recommended version of OS & Python:
- OS: Ubuntu 18.04.2 LTS
- Python: python3.7 (instructions to install python3.7).
pip install --upgrade pip
pip install -r requirements.txt
Download Solar-Energy, Electricity datasets from https://github.com/laiguokun/multivariate-time-series-data. Uncompress them and move them to the data folder.
Download the METR-LA dataset from Google Drive provided by Li et al.
mkdir -p data/METR-LA
python generate_training_data.py --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
python train_single_step.py --save ./model-solar-3.pt --data ./data/solar_AL.txt --num_nodes 137 --layers 4 --conv_kernel [6, 12] --hid_size 6 --batch_size 4 --epochs 30 --horizon 3
python train_single_step.py --save ./model-electricity-3.pt --data ./data/electricity.txt --num_nodes 321 --layers 5 --conv_kernel [12, 16] --hid_size 6 --batch_size 4 --epochs 30 --horizon 3
python train_multi_step.py --adj_data ./data/sensor_graph/adj_mx.pkl --data ./data/METR-LA --num_nodes 207 --conv_kernel [12, 16] --hid_size 6 --batch_size 4 --epochs 30 --horizon 3
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Under Review