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https://github.com/nnzhan/MTGNN
https://github.com/nnzhan/MTGNN
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/nnzhan/MTGNN
- Owner: nnzhan
- License: mit
- Created: 2020-05-25T02:28:11.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T22:00:30.000Z (over 1 year ago)
- Last Synced: 2024-08-02T06:20:45.665Z (5 months ago)
- Language: Python
- Size: 49.8 KB
- Stars: 778
- Watchers: 10
- Forks: 219
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-time-series - [Code
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README
# MTGNN
This is a PyTorch implementation of the paper: [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://arxiv.org/abs/2005.11650), published in KDD-2020.
## Requirements
The model is implemented using Python3 with dependencies specified in requirements.txt
## Data Preparation
### Multivariate time series datasets
Download Solar-Energy, Traffic, Electricity, Exchange-rate datasets from [https://github.com/laiguokun/multivariate-time-series-data](https://github.com/laiguokun/multivariate-time-series-data). Uncompress them and move them to the data folder.
### Traffic datasets
Download the METR-LA and PEMS-BAY dataset from [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g) provided by [Li et al.](https://github.com/liyaguang/DCRNN.git) . Move them into the data folder.
```
# Create data directories
mkdir -p data/{METR-LA,PEMS-BAY}
# METR-LA
python generate_training_data.py --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
# PEMS-BAY
python generate_training_data.py --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
```
## Model Training
### Single-step
* Solar-Energy
```
python train_single_step.py --save ./model-solar-3.pt --data ./data/solar_AL.txt --num_nodes 137 --batch_size 4 --epochs 30 --horizon 3
#sampling
python train_single_step.py --num_split 3 --save ./model-solar-sampling-3.pt --data ./data/solar_AL.txt --num_nodes 137 --batch_size 16 --epochs 30 --horizon 3
```
* Traffic
```
python train_single_step.py --save ./model-traffic3.pt --data ./data/traffic.txt --num_nodes 862 --batch_size 16 --epochs 30 --horizon 3
#sampling
python train_single_step.py --num_split 3 --save ./model-traffic-sampling-3.pt --data ./data/traffic --num_nodes 321 --batch_size 16 --epochs 30 --horizon 3
```
* Electricity
```
python train_single_step.py --save ./model-electricity-3.pt --data ./data/electricity.txt --num_nodes 321 --batch_size 4 --epochs 30 --horizon 3
#sampling
python train_single_step.py --num_split 3 --save ./model-electricity-sampling-3.pt --data ./data/electricity.txt --num_nodes 321 --batch_size 16 --epochs 30 --horizon 3
```
* Exchange-Rate
```
python train_single_step.py --save ./model/model-exchange-3.pt --data ./data/exchange_rate.txt --num_nodes 8 --subgraph_size 8 --batch_size 4 --epochs 30 --horizon 3
#sampling
python train_single_step.py --num_split 3 --save ./model-exchange-3.pt --data ./data/exchange_rate.txt --num_nodes 8 --subgraph_size 2 --batch_size 16 --epochs 30 --horizon 3
```
### Multi-step
* METR-LA
```
python train_multi_step.py --adj_data ./data/sensor_graph/adj_mx.pkl --data ./data/METR-LA --num_nodes 207
```
* PEMS-BAY
```
python train_multi_step.py --adj_data ./data/sensor_graph/adj_mx_bay.pkl --data ./data/PEMS-BAY/ --num_nodes 325
```
## Citation
```
@inproceedings{wu2020connecting,
title={Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks},
author={Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Chang, Xiaojun and Zhang, Chengqi},
booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
year={2020}
}
```