# Conditional GAN for timeseries generation

arXiv: https://arxiv.org/abs/2006.16477 · HTML: https://mlsanity.com/paper/2006.16477/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 30 of 37)

- [Vector Quantized Time Series Generation with a Bidirectional Prior Model](https://mlsanity.com/paper/2303.04743/) (2023)
- [TSGBench: Time Series Generation Benchmark](https://mlsanity.com/paper/2309.03755/) (2023)
- [Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing](https://mlsanity.com/paper/2211.07377/) (2022)
- [NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging](https://mlsanity.com/paper/2310.04429/) (2023)
- [Unsupervised Representation Learning for Time Series: A Review](https://mlsanity.com/paper/2308.01578/) (2023)
- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science](https://mlsanity.com/paper/2403.12636/) (2024)
- [Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey](https://mlsanity.com/paper/2310.10060/) (2023)
- [Towards Identifiability of Hierarchical Temporal Causal Representation Learning](https://mlsanity.com/paper/2510.18310/) (2025)
- [Causal Time Series Generation via Diffusion Models](https://mlsanity.com/paper/2509.20846/) (2025)
- [Review for Handling Missing Data with special missing mechanism](https://mlsanity.com/paper/2404.04905/) (2024)
- [Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation](https://mlsanity.com/paper/2505.17103/) (2025)
- [Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version)](https://mlsanity.com/paper/2312.11714/) (2023)
- [LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder](https://mlsanity.com/paper/2409.14622/) (2024)
- [Time Series (re)sampling using Generative Adversarial Networks](https://mlsanity.com/paper/2102.00208/) (2021)
- [The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches](https://mlsanity.com/paper/2209.12423/) (2022)
- [Transformer-based conditional generative adversarial network for multivariate time series generation](https://mlsanity.com/paper/2210.02089/) (2022)
- [Leveraging Statistical Shape Priors in GAN-based ECG Synthesis](https://mlsanity.com/paper/2211.02626/) (2022)
- [Towards Controllable Time Series Generation](https://mlsanity.com/paper/2403.03698/) (2024)
- [Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach](https://mlsanity.com/paper/2207.04306/) (2022)
- [Masked Generative Modeling with Enhanced Sampling Scheme](https://mlsanity.com/paper/2309.07945/) (2023)
- [GANs Conditioning Methods: A Survey](https://mlsanity.com/paper/2408.15640/) (2024)
- [CTBench: Cryptocurrency Time Series Generation Benchmark](https://mlsanity.com/paper/2508.02758/) (2025)
- [On the Use of Relative Validity Indices for Comparing Clustering Approaches](https://mlsanity.com/paper/2404.10351/) (2024)
- [Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/2207.01110/) (2022)
- [Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain](https://mlsanity.com/paper/2210.07617/) (2022)
- [Synthetic ALS-EEG Data Augmentation for ALS Diagnosis Using Conditional WGAN with Weight Clipping](https://mlsanity.com/paper/2506.16243/) (2025)
- [Wasserstein GAN: Deep Generation applied on Bitcoins financial time series](https://mlsanity.com/paper/2107.06008/) (2021)
- [Simulating Network Paths with Recurrent Buffering Units](https://mlsanity.com/paper/2202.13870/) (2022)
- [PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings](https://mlsanity.com/paper/2204.13597/) (2022)

## Abstract (arXiv)

It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. This work attempts to ease the frustration by proposing a new architecture, Time Series GAN (TSGAN), to model realistic time series data. We evaluate TSGAN on 70 data sets from a benchmark time series database. Our results demonstrate that TSGAN performs better than the competition both quantitatively using the Frechet Inception Score (FID) metric, and qualitatively when classification is used as the evaluation criteria.