Anomaly detection in multivariate time series data using deep ensemble models

Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, complex data streams in real time despite existing...

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Veröffentlicht in:	PloS one Jg. 19; H. 6; S. e0303890
Hauptverfasser:	Iqbal, Amjad, Amin, Rashid, Alsubaei, Faisal S., Alzahrani, Abdulrahman
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 06.06.2024
Schlagworte:	Algorithms Anomalies Artificial neural networks Biology and Life Sciences Clustering Complexity Computer and Information Sciences Data analysis Data transmission Datasets Decision making Deep Learning Engineering and Technology Forecasting Forecasts and trends Fraud Graph neural networks Industrial applications Learning strategies Long short-term memory Machine learning Manufacturing Multivariate Analysis Neural networks Neural Networks, Computer Physical Sciences Real time Recurrent neural networks Research and Analysis Methods Sequences Social Sciences Statistical analysis Time Factors Time series
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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