Dyn-GWN: Time-Series Forecasting using Time-varying Graphs with Applications to Finance and Traffic Prediction
This is the offical repo of the ICAIF 2023 paper Dyn-GWN: Time-Series Forecasting using Time-varying Graphs with Applications to Finance and Traffic Prediction
Dyn-GWN is an adaptation of GWN for dynamic graphs.
This code has been tested with PyTorch 2.0.
Scripts to run the algorithms are located in scripts/
. The current code supports the following datasets:
Financial: Stock Volatility,
Traffic: METR-LA, PEMS-BAY.
If you find DynGWN useful in your research, please consider citing the following paper.
@inproceedings{Ibrahim2023,
author = {Ibrahim, Shibal and Tell, Max and Mazumder, Rahul},
title = {Dyn-GWN: Time-Series Forecasting using Time-varying Graphs with Applications to Finance and Traffic Prediction},
year = {2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {4th ACM International Conference on AI in Finance},
numpages = {9},
keywords = {spatio-temporal modeling, time-series forecasting, time-varying graphs, graph neural networks},
location = {New York, NY, USA},
series = {ICAIF '23}
}