Multivariate Time Series Learning for Financial Data Using Graph Neural Network

This is code implementation of the project "Multivariate Time Series Learning for Financial Data Using Graph Neural Network" for KDD course.

Implementation based on https://github.com/nnzhan/MTGNN a PyTorch implementation of the paper: Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks.

----------- the following is not changed yet

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. Uncompress them and move them to the data folder.

Traffic datasets

Download the METR-LA and PEMS-BAY dataset from Google Drive or Baidu Yun provided by Li et al. . 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}
}

Peiyance/Time-Series