# TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2009.07769 · HTML: https://mlsanity.com/paper/2009.07769/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 30 of 55)

- [Large language models can be zero-shot anomaly detectors for time series?](https://mlsanity.com/paper/2405.14755/) (2024)
- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network](https://mlsanity.com/paper/2309.00638/) (2023)
- [When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection](https://mlsanity.com/paper/2312.11976/) (2023)
- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling](https://mlsanity.com/paper/2311.12550/) (2023)
- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [Are we certain it's anomalous?](https://mlsanity.com/paper/2211.09224/) (2022)
- [Detecting Anomalies within Time Series using Local Neural Transformations](https://mlsanity.com/paper/2202.03944/) (2022)
- [Harnessing Vision-Language Models for Time Series Anomaly Detection](https://mlsanity.com/paper/2506.06836/) (2025)
- [DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2310.08800/) (2023)
- [AER: Auto-Encoder with Regression for Time Series Anomaly Detection](https://mlsanity.com/paper/2212.13558/) (2022)
- [Impacts and Risk of Generative AI Technology on Cyber Defense](https://mlsanity.com/paper/2306.13033/) (2023)
- [Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data](https://mlsanity.com/paper/2401.04004/) (2024)
- [Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2408.03747/) (2024)
- [Deep Anomaly Detection on Tennessee Eastman Process Data](https://mlsanity.com/paper/2303.05904/) (2023)
- [Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection](https://mlsanity.com/paper/2202.12457/) (2022)
- [An Anomaly Detection Method for Satellites Using Monte Carlo Dropout](https://mlsanity.com/paper/2211.14938/) (2022)
- [ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN](https://mlsanity.com/paper/2308.06663/) (2023)
- [TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/2303.12952/) (2023)
- [A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series](https://mlsanity.com/paper/2405.03234/) (2024)
- [Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly](https://mlsanity.com/paper/2405.13995/) (2024)
- [M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding](https://mlsanity.com/paper/2504.15225/) (2025)
- [NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2101.02908/) (2021)
- [A Survey of Time Series Anomaly Detection Methods in the AIOps Domain](https://mlsanity.com/paper/2308.00393/) (2023)
- [AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence](https://mlsanity.com/paper/2508.03436/) (2025)
- [Pastprop-RNN: improved predictions of the future by correcting the past](https://mlsanity.com/paper/2106.13881/) (2021)
- [Anomaly Detection on IT Operation Series via Online Matrix Profile](https://mlsanity.com/paper/2108.12093/) (2021)
- [Identifying Cause-and-Effect Relationships of Manufacturing Errors using Sequence-to-Sequence Learning](https://mlsanity.com/paper/2205.02827/) (2022)
- [Denoising Architecture for Unsupervised Anomaly Detection in Time-Series](https://mlsanity.com/paper/2208.14337/) (2022)

## Abstract (arXiv)

Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations. Current state-of-the-art unsupervised machine learning methods for anomaly detection suffer from scalability and portability issues, and may have high false positive rates. In this paper, we propose TadGAN, an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs). To capture the temporal correlations of time series distributions, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. TadGAN is trained with cycle consistency loss to allow for effective time-series data reconstruction. We further propose several novel methods to compute reconstruction errors, as well as different approaches to combine reconstruction errors and Critic outputs to compute anomaly scores. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one. We compare our approach to 8 baseline anomaly detection methods on 11 datasets from multiple reputable sources such as NASA, Yahoo, Numenta, Amazon, and Twitter. The results show that our approach can effectively detect anomalies and outperform baseline methods in most cases (6 out of 11). Notably, our method has the highest averaged F1 score across all the datasets. Our code is open source and is available as a benchmarking tool.