A novel multi-level framework for anomaly detection in time series data

Anomaly detection is a challenging problem in science and engineering that appeals to numerous scholars. It is of great relevance to detect anomalies and analyze their potential implications. In this study, a multi-level anomaly detection framework with information granules of higher type and higher...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Netherlands), 2023-05, Vol.53 (9), p.10009-10026
Hauptverfasser:	Zhou, Yanjun, Ren, Huorong, Zhao, Dan, Li, Zhiwu, Pedrycz, Witold
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Anomalies Artificial Intelligence Clustering Computer Science Granular materials Machines Manufacturing Mechanical Engineering Performance indices Processes Synthetic data Time series
Online-Zugang:	Volltext
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