1 MAGNN

This repo is the official implementation for Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting.

1.1 The framework of MAGNN

2 Prerequisites

Python 3.6.12
PyTorch 1.0.0
math, sklearn, numpy

3 Datasets

To evaluate the performance of MAGNN, we conduct experiments on four public benchmark datasets：Solar-Energy, Traffic, Electricity, and Exchange-Rate.

3.1 Solar-Energy

This dataset contains the collected solar power from the National Renewable Energy Laboratory, which is sampled every 10 minutes from 137 PV plants in Alabama State in 2007.

3.2 Traffic

This dataset contains the road occupancy rates (between 0 and 1) from the California Department of Transportation, which is hourly aggregated from 862 sensors in San Francisco Bay Area from 2015 to 2016.

3.3 Electricity

This dataset contains the electricity consumption from the UCI Machine Learning Repository, which is hourly aggregated from 321 clients from 2012 to 2014.

3.4 Exchange-Rate

This dataset contains the exchange rates of eight countries, which is sampled daily from 1990 to 2016.

4 Running

4.1 Install all dependencies listed in prerequisites

4.2 Download the dataset

4.3 Hyper-parameters search with NNI

# Hyper-parameters search with NNI
 nnictl create --config config.yml --port 8080

4.4 Training

# Train on Solar-Energy
CUDA_LAUNCH_BLOCKING=1 python train.py --save ./model-solar-1.pt --data solar-energy/solar-energy.txt --num_nodes 8 --batch_size 4 --epochs 50 --horizon 3
# Train on Traffic
CUDA_LAUNCH_BLOCKING=1 python train.py --save ./model-traffic-3.pt --data traffic/traffic.txt --num_nodes 8 --batch_size 4 --epochs 50 --horizon 3
# Train on Electricity
CUDA_LAUNCH_BLOCKING=1 python train.py --save ./model-electricity-3.pt --data electricity/electricity.txt --num_nodes 8 --batch_size 4 --epochs 50 --horizon 3
# Train on Exchange-Rate
CUDA_LAUNCH_BLOCKING=1 python train.py --save ./model-exchange-4.pt --data exchange_rate/exchange_rate.txt --num_nodes 8 --batch_size 4 --epochs 50 --horizon 3

5 Citation

Please cite the following paper if you use the code in your work:

@Inproceedings{616B,
  title={Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting.},
  author={Ling Chen, Donghui Chen, Zongjiang Shang, Youdong Zhang, Bo Wen, and Chenghu Yang.},
  booktitle={},
  year={2021}
}

GKDGKD/MAGNN