umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Anomaly Detection in Signaling Data Streams: A Time-Series Approach
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This work has aimed to develop a method which can be used in order to detect anomalies in signaling data streams at a telecommunication company. It has been done by modeling and evaluating three prediction models and two detection methods. The prediction models which have been implemented are Autoregressive Integrated Moving Average (ARIMA), Holt-Winters and a Benchmark model, furthermore have two detection methods been tested; Method 1 (M1), which is based on a robust evaluation of previous prediction errors and Method 2 (M2), which is based on the standard deviation in previous data. From the evaluation of the work, we could conclude that the best performing combination of prediction- and detection methods was achieved with a modified Benchmark model and M1- detection.

Place, publisher, year, edition, pages
2017. , p. 47
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-139773OAI: oai:DiVA.org:umu-139773DiVA, id: diva2:1143628
External cooperation
Tele2 IoT
Presentation
2017-06-01, 14:00 (English)
Supervisors
Examiners
Available from: 2017-09-22 Created: 2017-09-21 Last updated: 2017-09-22Bibliographically approved

Open Access in DiVA

fulltext(1704 kB)271 downloads
File information
File name FULLTEXT01.pdfFile size 1704 kBChecksum SHA-512
5e994aff772c445724fa1309aaf2ca42a4c67bdca5c11fcaf587c03546bc5137a51128ee203ab90811fa747155c053dd0592a11eb07ea3aa0377754193bd9cd6
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Thorén, SofiaSörberg, Richard
By organisation
Department of Mathematics and Mathematical Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 271 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 592 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf