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 Diagnostics Data with Natural Fluctuations
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Anomalidetektering i diagnostikdata med naturliga variationer (Swedish)
Abstract [en]

In this thesis, the red hot topic anomaly detection is studied, which is a subtopic in machine learning. The company, Procera Networks, supports several broadband companies with IT-solutions and would like to detected errors in these systems automatically. This thesis investigates and devises methods and algorithms for detecting interesting events in diagnostics data. Events of interest include: short-term deviations (a deviating point), long-term deviations (a distinct trend) and other unexpected deviations. Three models are analyzed, namely Linear Predictive Coding, Sparse Linear Prediction and Wavelet Transformation. The final outcome is determined by the gap to certain thresholds. These thresholds are customized to fit the model as well as possible.

Abstract [sv]

I den här rapporten kommer det glödheta området anomalidetektering studeras, vilket tillhör ämnet Machine Learning. Företaget där arbetet utfördes på heter Procera Networks och jobbar med IT-lösningar inom bredband till andra företag. Procera önskar att kunna upptäcka fel hos kunderna i dessa system automatiskt. I det här projektet kommer olika metoder för att hitta intressanta företeelser i datatraffiken att genomföras och forskas kring. De mest intressanta företeelserna är framfärallt snabba avvikelser (avvikande punkt) och färändringar äver tid (trender) men också andra oväntade mänster. Tre modeller har analyserats, nämligen Linear Predictive Coding, Sparse Linear Prediction och Wavelet Transform. Det slutgiltiga resultatet från modellerna är grundat på en speciell träskel som är skapad fär att ge ett så bra resultat som mäjligt till den undersäkta modellen..

Place, publisher, year, edition, pages
2015.
Series
TRITA-MAT-E, 2015:46
Keyword [en]
Machine learning, anomaly detection, fault detection, Linear Predictive Coding, Sparse Linear Prediction, Wavelet Transformation
Keyword [sv]
Maskinlärning, anomalidetektering, feldetektering, Linear Predictive Coding, Sparse Linear Prediction, Wavelet-transformation
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-170237OAI: oai:DiVA.org:kth-170237DiVA: diva2:827694
Subject / course
Systems Engineering
Educational program
Master of Science - Mathematics
Supervisors
Examiners
Available from: 2015-06-29 Created: 2015-06-29 Last updated: 2015-06-29Bibliographically approved

Open Access in DiVA

fulltext(4971 kB)127 downloads
File information
File name FULLTEXT01.pdfFile size 4971 kBChecksum SHA-512
62c2f6f14622dc071e2c2d3f6504b9178316b2dac919659479af7d3c59532c3c8b1764b3dda5d2673c7b15c555dccecd999c30d04780ddba6536a5b4d7ac6c62
Type fulltextMimetype application/pdf

By organisation
Optimization and Systems Theory
Computational Mathematics

Search outside of DiVA

GoogleGoogle Scholar
Total: 127 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: 246 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