TimeGANs for synthetic data generation in cyber-physical systems using Tensorflow
-
Updated
Dec 9, 2022 - Jupyter Notebook
TimeGANs for synthetic data generation in cyber-physical systems using Tensorflow
TensorFlow 2.X implementation of TimeGAN
The SWAN-SF dataset is now fully preprocessed, optimized, and ready for binary classification tasks. Our team is excited to release the enhanced version of the SWAN-SF dataset across all five partitions.
This is the code related to my MSc thesis at the Norwegian University of Science and Technology (NTNU). The MSc program is Electronic system design and innovation with a specialization in signal processing. The goal of this thesis is to explore and compare the state of the art solution to more traditional models for time series generation.
These notebooks provide a comprehensive workflow, from start to finish, for processing and analyzing the SWAN-SF dataset. They include detailed steps for reading the dataset files, performing full preprocessing, and executing classification.
Studies of chaotic systems generation with GANs
Add a description, image, and links to the timegan topic page so that developers can more easily learn about it.
To associate your repository with the timegan topic, visit your repo's landing page and select "manage topics."