# TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2008.09567 · HTML: https://mlsanity.com/paper/2008.09567/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 29 of 29)

- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [MemStream: Memory-Based Streaming Anomaly Detection](https://mlsanity.com/paper/2106.03837/) (2021)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2310.08800/) (2023)
- [Impacts and Risk of Generative AI Technology on Cyber Defense](https://mlsanity.com/paper/2306.13033/) (2023)
- [Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies](https://mlsanity.com/paper/2505.20765/) (2025)
- [Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data](https://mlsanity.com/paper/2401.04004/) (2024)
- [Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection](https://mlsanity.com/paper/2202.12457/) (2022)
- [ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN](https://mlsanity.com/paper/2308.06663/) (2023)
- [Explaining Time Series Classification Predictions via Causal Attributions](https://mlsanity.com/paper/2405.15871/) (2024)
- [Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study](https://mlsanity.com/paper/2411.08341/) (2024)
- [QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients](https://mlsanity.com/paper/2509.06516/) (2025)
- [GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs](https://mlsanity.com/paper/2106.15504/) (2021)
- [MadSGM: Multivariate Anomaly Detection with Score-based Generative Models](https://mlsanity.com/paper/2308.15069/) (2023)
- [Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach](https://mlsanity.com/paper/2310.17091/) (2023)
- [A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection](https://mlsanity.com/paper/2511.18739/) (2025)
- [On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems](https://mlsanity.com/paper/2605.07860/) (2026)
- [Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection](https://mlsanity.com/paper/2202.10001/) (2022)
- [DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density Estimation](https://mlsanity.com/paper/2210.02449/) (2022)
- [Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy Sources](https://mlsanity.com/paper/2305.02323/) (2023)
- [Accurate and fast anomaly detection in industrial processes and IoT environments](https://mlsanity.com/paper/2404.17925/) (2024)
- [Anomalous Change Point Detection Using Probabilistic Predictive Coding](https://mlsanity.com/paper/2405.15727/) (2024)
- [Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2509.17235/) (2025)
- [Selective Denoising Diffusion Model for Time Series Anomaly Detection](https://mlsanity.com/paper/2602.23662/) (2026)
- [Learning Unified Representations of Normalcy for Time Series Anomaly Detection](https://mlsanity.com/paper/2605.09685/) (2026)
- [Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices](https://mlsanity.com/paper/2607.12599/) (2026)

## Abstract (arXiv)

Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security. Recently, Generative Adversarial Networks (GAN) have gained attention for generation and anomaly detection in image domain. In this paper, we propose a novel GAN-based unsupervised method called TAnoGan for detecting anomalies in time series when a small number of data points are available. We evaluate TAnoGan with 46 real-world time series datasets that cover a variety of domains. Extensive experimental results show that TAnoGan performs better than traditional and neural network models.