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Article

Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Department of Computer Science, AGH University of Science and Technology, Adama Mickiewicza 30, 30-059 Krakow, Poland
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Academic Editors: Andrea Prati, Carlos A. Iglesias, Luis Javier García Villalba and Vincent A. Cicirello
Entropy 2021, 23(11), 1466; https://doi.org/10.3390/e23111466
Received: 2 October 2021 / Revised: 22 October 2021 / Accepted: 2 November 2021 / Published: 6 November 2021
(This article belongs to the Special Issue Probabilistic Methods for Deep Learning)
Multivariate time series anomaly detection is a widespread 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 that automate the process of detecting anomalies are crucial in modern failure prevention systems. Therefore, many machine learning models have been designed to address this problem. Mostly, they are autoencoder-based architectures with some generative adversarial elements. This work shows a framework that incorporates neuroevolution methods to boost the anomaly detection scores of new and already known models. The presented approach adapts evolution strategies for evolving an ensemble model, in which every single model works on a subgroup of data sensors. The next goal of neuroevolution is to optimize the architecture and hyperparameters such as the window size, the number of layers, and the layer depths. The proposed framework shows that it is possible to boost most anomaly detection deep learning models in a reasonable time and a fully automated mode. We ran tests on the SWAT and WADI datasets. To the best of 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. View Full-Text
Keywords: neuroevolution; anomaly detection; ensemble model; CNN; time series; deep learning neuroevolution; anomaly detection; ensemble model; CNN; time series; deep learning
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MDPI and ACS Style

Faber, K.; Pietron, M.; Zurek, D. Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection. Entropy 2021, 23, 1466. https://doi.org/10.3390/e23111466

AMA Style

Faber K, Pietron M, Zurek D. Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection. Entropy. 2021; 23(11):1466. https://doi.org/10.3390/e23111466

Chicago/Turabian Style

Faber, Kamil, Marcin Pietron, and Dominik Zurek. 2021. "Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection" Entropy 23, no. 11: 1466. https://doi.org/10.3390/e23111466

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