This repository contains additional experiments and code in the paper Evaluating generation of chaotic time series by convolutional generative adversarial networks by Y. Tanaka and Y. Yamaguti.

This repository contains code to define and train GAN models for generating chaotic time series and to analyze the generated time series nonlinear time series.

Online Supplementary Material

The online supplementary material (PDF file) is available here.

Requirements

These codes were developed using Tensorflow 2.7 and Python 3.9.10.

Files

• chaosgan.py trains Generator and Discriminator.

• chaosgan_analysis.py performs analysis of GAN-generated time-series. (including results in Fig. 1 - Fig. 3 )

• chaosgan_errors.py performs analysis of errors (including results in Fig. 4).

• chaosmap.py defines several chaos maps.

• models.py defines GAN models

• nltsa.py implements nonlinear time series analysis algorithms.

• tools.py defines trajectory generator class, transition error calculation class, and plotting functions.

• parzen.py implements Parzen window (kernel density estimation) method and estimation of KL divergence.

Usage

Training models

python chaosgan.py

Running analysis

After the training, run

python chaosgan_analysis.py

and then:

python chaosgan_errors.py

Citation

If you use this code in your research, please cite our paper:

@misc{tanaka2023evaluating,
      title={Evaluating generation of chaotic time series by convolutional generative adversarial networks}, 
      author={Yuki Tanaka and Yutaka Yamaguti},
      year={2023},
      eprint={2305.16729},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Model Architecture

The graphs shows architecture of the generator and discriminator. These graphs are generated by tf.keras.utils.plot_model().

Generator

Discriminator