Outliers detection in non-stationary time-series: Support vector machine versus principal component analysis

P. Gil, H. Martins, A. Cardoso, L. Palma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper aims at comparing two local outliers detection techniques. One is based on a Least Squares Support Vector Machine technique within a sliding window-based learning algorithm. A modification is proposed to improve its performance in non-stationary time-series. The second method relies on the Principal Component Analysis theory along with a robust orthonormal projection approximation subspace tracking with rank-1 modification. The comparative performance of these methods are assessed through simulations using a non stationary time-series generated with a nonlinear input-output model.
Original languageEnglish
Title of host publication2016 12th IEEE International Conference on Control and Automation (ICCA)
PublisherIEEE
Pages701-706
Number of pages6
ISBN (Electronic)978-1-5090-1738-6
DOIs
Publication statusPublished - 1 Jun 2016

Publication series

NameIEEE International Conference on Control and Automation ICCA
PublisherIEEE
ISSN (Electronic)1948-3449

Keywords

  • WIRELESS SENSOR NETWORKS
  • ANOMALY DETECTION
  • ALGORITHM

Fingerprint Dive into the research topics of 'Outliers detection in non-stationary time-series: Support vector machine versus principal component analysis'. Together they form a unique fingerprint.

Cite this