Unsupervised Anomaly Detection on Multi-Process Event Time Series

Unsupervised Anomaly Detection on Multi-Process Event Time Series

Establishing whether the observed data are anomalous or not is an important task that has been widely investigated in literature, and it becomes an even more complex problem if combined with high dimensional representations and multiple sources independently generating the patterns to be analyzed. T...

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Bibliographic Details
Main Author:	Vendramin, Nicoló
Format:	Others
Language:	English
Published:	KTH, Skolan för elektroteknik och datavetenskap (EECS) 2018
Subjects:	Anomaly Detection Recurrent Neural Networks Time Series Analysis Unsupervised Learning Anomalitetsdetektering Återkommande neurala nätverk Tidsserieanalys Oövervakat lärande Computer and Information Sciences Data- och informationsvetenskap
Online Access:	http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254885

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