Thesis Details

Anomaly Detection in Generated Incident Ticket Volumes

Bachelor's Thesis Student: Šurina Timotej Academic Year: 2018/2019 Supervisor: Trchalík Roman, Mgr., Ph.D.
Czech title
Detekce anomálií v množství generovaných záznamů o incidentech
Language
English
Abstract

This bachelor thesis deals with the issue of time series anomaly detection. It presents methods STL decomposition, ARIMA, Exponential Smoothing and LSTM Networks. The aim is to use these methods to create an algorithm that can analyze the trend in a volume of generated incident tickets and detect anomalies form the trend. The solution was created based on a dataset provided by firm AT&T Global Network Services Czech Republic s.r.o. and implemented in the Python programming language.

Keywords

Anomaly, anomaly detection, time series, machine learning, statistical methods, STL decomposition, ARIMA, Exponential Smoothing, LSTM Networks, ticket

Department
Department of Information Systems FIT BUT
Degree Programme
Information Technology
Files
Status
defended, grade E
Date
10 June 2019
Reviewer
Rychlý Marek, RNDr., Ph.D.
Committee
Smrž Pavel, doc. RNDr., Ph.D. (DCGM FIT BUT), předseda
Fučík Otto, doc. Dr. Ing. (DCSY FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Szőke Igor, Ing., Ph.D. (DCGM FIT BUT), člen
Veselý Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
Citation
ŠURINA, Timotej. Anomaly Detection in Generated Incident Ticket Volumes. Brno, 2019. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2019-06-10. Supervised by Trchalík Roman. Available from: https://www-dev.fit.vutbr.cz/study/thesis/21169/
BibTeX 
@bachelorsthesis{FITBT21169,
    author = "Timotej \v{S}urina",
    type = "Bachelor's thesis",
    title = "Anomaly Detection in Generated Incident Ticket Volumes",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2019,
    location = "Brno, CZ",
    language = "english",
    url = "https://www.fit.vut.cz/study/thesis/21169/"
}

Theses

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