# Ensemble neuroevolution based approach for multivariate time series anomaly detection

arXiv: https://arxiv.org/abs/2108.03585 · HTML: https://mlsanity.com/paper/2108.03585/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Evolving Deep Neural Networks](https://mlsanity.com/paper/1703.00548/) (2017)
- [Evolving Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1710.10741/) (2017)
- [Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges](https://mlsanity.com/paper/2006.05415/) (2020)
- [Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data](https://mlsanity.com/paper/2002.03843/) (2020)

## Built on by (most cited first, 8 of 8)

- [Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction](https://mlsanity.com/paper/2112.15445/) (2021)
- [AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection](https://mlsanity.com/paper/2305.16497/) (2023)
- [Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection](https://mlsanity.com/paper/2112.05640/) (2021)
- [Neuroevolution deep learning architecture search for estimation of river surface elevation from photogrammetric Digital Surface Models](https://mlsanity.com/paper/2112.12510/) (2021)
- [Satellite Anomaly Detection Using Variance Based Genetic Ensemble of Neural Networks](https://mlsanity.com/paper/2302.05525/) (2023)
- [Towards efficient deep autoencoders for multivariate time series anomaly detection](https://mlsanity.com/paper/2403.02429/) (2024)
- [AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework](https://mlsanity.com/paper/2404.07968/) (2024)
- [Evolutionary fine tuning of quantized convolution-based deep learning models](https://mlsanity.com/paper/2605.05228/) (2026)

## Abstract (arXiv)

Multivariate time series anomaly detection is a very common problem in the field of failure prevention. Fast prevention means lower repair costs and losses. The amount of sensors in novel industry systems makes the anomaly detection process quite difficult for humans. Algorithms which automates the process of detecting anomalies are crucial in modern failure-prevention systems. Therefore, many machine and deep learning models have been designed to address this problem. Mostly, they are autoencoder-based architectures with some generative adversarial elements. In this work, a framework is shown which incorporates neuroevolution methods to boost the anomaly-detection scores of new and already known models. The presented approach adapts evolution strategies for evolving ensemble model, in which every single model works on a subgroup of data sensors. The next goal of neuroevolution is to optimise architecture and hyperparameters like window size, the number of layers, layer depths, etc. The proposed framework shows that it is possible to boost most of the anomaly detection deep learning models in a reasonable time and a fully automated mode. The tests were run on SWAT and WADI datasets. To our knowledge, this is the first approach in which an ensemble deep learning anomaly detection model is built in a fully automatic way using a neuroevolution strategy.