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Unsupervised real-time anomaly detection on streaming data for large-scale application deployments
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Oövervakad avvikelsedetektering på strömmande realtidsdata för storskalig applikationsutbyggnad (Swedish)
Abstract [en]

Anomaly detection is the classification of data points that do not adhere to the familiar pattern; in previous studies there existed a need for extensive human interactions with either labelling or sorting normal and abnormal data from one another. In this thesis, we want to go one step further and apply machine learning techniques on time-series data in order to have a deeper understanding of the properties of a given data point without any sorting and labelling. In this thesis, a method is presented that can successfully find anomalies in both real and synthetic datasets. The method uses a combination of three algorithms from various disciplines, Hierarchical temporal memory and Restricted Boltzmann machines from machine learning and Autoregressive integrated moving average from regression. Each algorithm is specialised in finding a particular type of anomalies. The combination finds all anomalies with little to no gap from the occurrence of an anomaly to its detection.

Abstract [sv]

Avvikelsedetektering är klassificeringen av datapunkter som inte följer det kända mönstret; tidigare studier krävde omfattande mänskliga interaktioner med antingen märkning eller sortering av normala och onormala data från varandra. I detta examensarbete vill vi gå ett steg längre och tillämpa maskininlärningsteknik på tidsseriedata för att få en djupare förståelse för egenskaperna hos en given datapunkt utan någon sortering och märkning. I detta examensarbete presenteras en metod som framgångsrikt kan hitta anomalier i både reella och syntetiska dataset. Metoden använder en kombination av tre algoritmer från olika discipliner, Hierarchical temporal memory och Restricted Boltzmann machines från maskininlärning och Autoregressive integrated moving average från regression. Varje algoritm är specialiserad på att hitta en viss typ av anomalier. Kombinationen finner alla anomalier med liten eller inget avstånd från förekomst av en anomali till dess detektion.

Place, publisher, year, edition, pages
2019. , p. 65
Series
TRITA-EECS-EX ; 2019:516
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262681OAI: oai:DiVA.org:kth-262681DiVA, id: diva2:1361947
External cooperation
Hive Streaming AB
Supervisors
Examiners
Available from: 2019-11-11 Created: 2019-10-17 Last updated: 2019-11-11Bibliographically approved

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