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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http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254885

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