ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation

Abstract

Generating time series data using Generative Adversarial Networks (GANs) presents several prevalent challenges, such as slow convergence, information loss in embedding spaces, instability, and performance variability depending on the series length. To tackle these obstacles, we introduce a robust framework aimed at addressing and mitigating these issues effectively. This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs. This framework benefits from a time series-based loss function and oversight from a supervisory network, both of which capture the stepwise conditional distributions of the data effectively. The generator functions within the latent space, while the discriminator offers essential feedback based on the feature space. Moreover, we introduce an early generation algorithm and an improved neural network architecture to enhance stability and ensure effective generalization across both short and long time series. Through joint training, our framework consistently outperforms existing benchmarks, generating high-quality time series data across a range of real and synthetic datasets with diverse characteristics.

How to Cite

If you find our method useful in your research or work, we kindly ask that you cite our paper. The paper associated with this repository has been accepted at ICMLA 2024 and is now available on arXiv: https://arxiv.org/abs/2409.14013

Here is the BibTeX citation for your reference:

@misc{chronogan2024,
      title={ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation}, 
      author={MohammadReza EskandariNasab and Shah Muhammad Hamdi and Soukaina Filali Boubrahimi},
      year={2024},
      eprint={2409.14013},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2409.14013}, 
}

We appreciate your citation!

Installation

Clone the repository and install dependencies:

git clone https://github.com/samresume/ChronoGAN.git
cd ChronoGAN
pip install -r requirements.txt

Usage

To get started, run the tutorial notebook:

jupyter notebook tutorial.ipynb

Files

• chronogan.py: Main implementation of the ChronoGAN model.
• data_loading.py: Functions for loading and preprocessing data.
• utils.py: Helper utilities for the model.

License

This project is licensed under the MIT License.