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Anomaly Detection Using an Ensemble of Multi-Point LSTMs

Authors :
Geonseok Lee
Youngju Yoon
Kichun Lee
Source :
Entropy, Vol 25, Iss 11, p 1480 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

As technologies for storing time-series data such as smartwatches and smart factories become common, we are collectively accumulating a great deal of time-series data. With the accumulation of time-series data, the importance of time-series abnormality detection technology that detects abnormal patterns such as Cyber-Intrusion Detection, Fraud Detection, Social Networks Anomaly Detection, and Industrial Anomaly Detection is emerging. In the past, time-series anomaly detection algorithms have mainly focused on processing univariate data. However, with the development of technology, time-series data has become complicated, and corresponding deep learning-based time-series anomaly detection technology has been actively developed. Currently, most industries rely on deep learning algorithms to detect time-series anomalies. In this paper, we propose an anomaly detection algorithm with an ensemble of multi-point LSTMs that can be used in three cases of time-series domains. We propose our anomaly detection model that uses three steps. The first step is a model selection step, in which a model is learned within a user-specified range, and among them, models that are most suitable are automatically selected. In the next step, a collected output vector from M LSTMs is completed by stacking ensemble techniques of the previously selected models. In the final step, anomalies are finally detected using the output vector of the second step. We conducted experiments comparing the performance of the proposed model with other state-of-the-art time-series detection deep learning models using three real-world datasets. Our method shows excellent accuracy, efficient execution time, and a good F1 score for the three datasets, though training the LSTM ensemble naturally requires more time.

Details

Language :
English
ISSN :
10994300
Volume :
25
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Entropy
Publication Type :
Academic Journal
Accession number :
edsdoj.1d44db97da5b4231ab19b24d3efc49de
Document Type :
article
Full Text :
https://doi.org/10.3390/e25111480