GenFormer: A Deep-Learning-Based Approach for Generating Multivariate Stochastic Processes
Stochastic generators are essential to produce synthetic realizations that preserve target statistical properties. We propose GenFormer, a stochastic generator for spatio-temporal multivariate stochastic processes, enlighted by Transformer-based deep learning models used for time series forecasting.
Our numerical examples involving numerous spatial locations and simulation over a long time horizon demonstrate that synthetic realizations produced by GenFormer can be reliably utilized in downstream applications due to the superior performance of deep learning models for complex and high-dimensional tasks.
- Install Python 3.6, PyTorch 1.9.0.
- Download data. You can obtain all the data from [Google Drive](https://drive.google.com/drive/u/0/folders/1JLjhje3j-RJ4gNKtbyEE4afWR5S32idH for SDE example and https://drive.google.com/drive/u/0/folders/1Sj3Jy-Xx8jgsNOaPjM0GdP5dMWPsj5Sb for wind example). All the datasets are well pre-processed and can be used easily.
- Train the model and run the simulations. We provide the notebooks for two experiments under the folder
./notebooks.
If you find this repo useful, please cite our paper.
@article{genformer_paper,
author = "Zhao, H. and Uy, W.I.T.",
title = "GenFormer: A Deep-Learning-Based Approach for Generating Multivariate Stochastic Processes",
journal = " arXiv:2402.02010",
year = 2024
}
If you have any questions or want to use the code, please contact zhaohr1990@gmail.com
We appreciate the following github repos a lot for their valuable code base or datasets: