flaviagiammarino/time-gan-tensorflow
TensorFlow implementation of TimeGAN model for synthetic time series generation with generative adversarial networks.
This project helps data scientists and machine learning engineers create realistic synthetic time series data. You provide it with your existing real-world time series, and it generates entirely new, artificial time series that mimic the statistical patterns and trends of your original data. This is useful for expanding datasets, testing models, or sharing data while protecting privacy.
No commits in the last 6 months.
Use this if you need to generate artificial time series data that closely resembles your real-world observations, for tasks like model training or privacy-preserving data sharing.
Not ideal if you need to predict future values of an existing time series or forecast trends.
Stars
33
Forks
9
Language
Python
License
MIT
Category
Last pushed
Apr 15, 2024
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