# MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

arXiv: https://arxiv.org/abs/2310.18257 · HTML: https://mlsanity.com/paper/2310.18257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks](https://mlsanity.com/paper/2203.13464/) (2022)

## Built on by (most cited first, 2 of 2)

- [Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures](https://mlsanity.com/paper/2507.14387/) (2025)
- [GRAD: Real-Time Gated Recurrent Anomaly Detection in Autonomous Vehicle Sensors Using Reinforced EMA and Multi-Stage Sliding Window Techniques](https://mlsanity.com/paper/2510.23327/) (2025)

## Abstract (arXiv)

The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure(MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant reconstruction score consisting on discrimination and reconstruction loss is taken into account. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1 score.