# jsyoon0823/TimeGAN

Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

Repository: https://github.com/jsyoon0823/TimeGAN
Canonical: https://ross.abutalabs.com/products/timegan
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2026-02-05T17:06:00+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 66, release rhythm 35, longevity 100
- inputs: {"age_days": 2322, "days_push": 209, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1060, forks 326 (observed 2026-08-28T04:03:25.762234+00:00)

## What it is
Reference implementation of TimeGAN, a generative adversarial network framework for generating synthetic time-series data, published at NeurIPS 2019. It includes training pipelines, evaluation metrics (discriminative, predictive, PCA/t-SNE visualization), and tutorial notebooks for sine, stock, and energy datasets.

## Use cases
- generate synthetic time series data
- augment small time-series datasets for training
- evaluate synthetic time series quality
- reproduce TimeGAN paper results
- benchmark GAN models on sequential data
- create fake stock or sensor data for testing

## When to choose
- you need synthetic sequential data for privacy or augmentation
- you want a research baseline for time-series GANs
- you want to reproduce the NeurIPS 2019 TimeGAN results

## When to avoid
- you need production-grade, well-supported tooling
- you need non-time-series tabular data generation
- you require a permissive license (license is non-standard)

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, data-generation
- domain: machine-learning, time-series, artificial-intelligence, data-science
- platform: python
- tags: timegan, gan, synthetic-data, time-series-generation, research-code, neurips-2019

## Member repositories
- jsyoon0823/TimeGAN (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:25.762234+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:56:57.468858+00:00, confidence not recorded.
  - readme: https://github.com/jsyoon0823/TimeGAN (fetched 2026-08-28T04:03:25.762234+00:00, sha 2a0b477eead7)
- Data as of 2026-08-30T08:39:29.467469+00:00.