A support vector machine based technique for online detection of outliers in transient time series

Hugo Martins, Luis Palma, Alberto Cardoso, Paulo Gil

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

9 Citations (Scopus)

Abstract

This paper deals with online detection and accommodation of outliers in transient time series by appealing to a machine learning technique. The methodology is based on a Least Squares Support Vector Machine technique together with a sliding window-based learning algorithm. A modification to this method is proposed so as to extend its application to transient raw data collected from transmitters attached to a Wireless Sensor Network. The performance of two approaches are compared on a particular controlled data set.
Original languageEnglish
Title of host publication2015 10th Asian Control Conference (ASCC)
Subtitle of host publicationEmerging Control Techniques for a Sustainable World
EditorsH. Selamat , H. R. H. Ramli , A. A. M. Faudzi , R. Z. A. Rahman , A. J. Ishak , A. C. Soh , S. A. Ahmad
Place of PublicationNew York
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-4799-7862-5
ISBN (Print)978-1-4799-7863-2
DOIs
Publication statusPublished - 2015
Event10th Asian Control Conference (ASCC 2015) - Kota Kinabalu, Malaysia
Duration: 31 May 20153 Jun 2015
Conference number: 10th

Conference

Conference10th Asian Control Conference (ASCC 2015)
Abbreviated titleASCC 2015
CountryMalaysia
CityKota Kinabalu
Period31/05/153/06/15

Keywords

  • WIRELESS SENSOR NETWORKS
  • ANOMALY DETECTION

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    Martins, H., Palma, L., Cardoso, A., & Gil, P. (2015). A support vector machine based technique for online detection of outliers in transient time series. In H. Selamat , H. R. H. Ramli , A. A. M. Faudzi , R. Z. A. Rahman , A. J. Ishak , A. C. Soh , & S. A. Ahmad (Eds.), 2015 10th Asian Control Conference (ASCC): Emerging Control Techniques for a Sustainable World [7244794] New York: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ascc.2015.7244794