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

Hugo Martins; Luis Palma; Alberto Cardoso; Paulo Gil

doi:10.1109/ascc.2015.7244794

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 proceeding › Conference contribution › peer-review

20 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 language	English
Title of host publication	2015 10th Asian Control Conference (ASCC)
Subtitle of host publication	Emerging Control Techniques for a Sustainable World
Editors	H. Selamat , H. R. H. Ramli , A. A. M. Faudzi , R. Z. A. Rahman , A. J. Ishak , A. C. Soh , S. A. Ahmad
Place of Publication	New York
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	978-1-4799-7862-5
ISBN (Print)	978-1-4799-7863-2
DOIs	https://doi.org/10.1109/ascc.2015.7244794
Publication status	Published - 2015
Event	10th Asian Control Conference (ASCC 2015) - Kota Kinabalu, Malaysia Duration: 31 May 2015 → 3 Jun 2015 Conference number: 10th

Conference

Conference	10th Asian Control Conference (ASCC 2015)
Abbreviated title	ASCC 2015
Country/Territory	Malaysia
City	Kota Kinabalu
Period	31/05/15 → 3/06/15

Keywords

WIRELESS SENSOR NETWORKS
ANOMALY DETECTION

Access to Document

10.1109/ascc.2015.7244794Licence: Unspecified

Cite this

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 Article 7244794 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ascc.2015.7244794

Martins, Hugo ; Palma, Luis ; Cardoso, Alberto et al. / A support vector machine based technique for online detection of outliers in transient time series. 2015 10th Asian Control Conference (ASCC): Emerging Control Techniques for a Sustainable World. editor / H. Selamat ; H. R. H. Ramli ; A. A. M. Faudzi ; R. Z. A. Rahman ; A. J. Ishak ; A. C. Soh ; S. A. Ahmad . New York : Institute of Electrical and Electronics Engineers (IEEE), 2015.

@inproceedings{972077c4b7af4ee5a25d11f88bfd65b6,

title = "A support vector machine based technique for online detection of outliers in transient time series",

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.",

keywords = "WIRELESS SENSOR NETWORKS , ANOMALY DETECTION",

author = "Hugo Martins and Luis Palma and Alberto Cardoso and Paulo Gil",

note = "Sem PDF conforme Despacho. This work has been partially supported by Project CENTRO-07-ST24-FEDER-002003 (iCIS-Intelligent Computing in the Internet of Services).; 10th Asian Control Conference (ASCC 2015), ASCC 2015 ; Conference date: 31-05-2015 Through 03-06-2015",

year = "2015",

doi = "10.1109/ascc.2015.7244794",

language = "English",

isbn = "978-1-4799-7863-2",

editor = "{Selamat }, H. and {Ramli }, {H. R. H.} and {Faudzi }, {A. A. M.} and {Rahman }, {R. Z. A.} and {Ishak }, {A. J.} and {Soh }, {A. C.} and {Ahmad }, {S. A.}",

booktitle = "2015 10th Asian Control Conference (ASCC)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

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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 , HRH Ramli , AAM Faudzi , RZA Rahman , AJ Ishak , AC Soh & SA Ahmad (eds), 2015 10th Asian Control Conference (ASCC): Emerging Control Techniques for a Sustainable World., 7244794, Institute of Electrical and Electronics Engineers (IEEE), New York, 10th Asian Control Conference (ASCC 2015), Kota Kinabalu, Malaysia, 31/05/15. https://doi.org/10.1109/ascc.2015.7244794

A support vector machine based technique for online detection of outliers in transient time series. / Martins, Hugo; Palma, Luis; Cardoso, Alberto et al.
2015 10th Asian Control Conference (ASCC): Emerging Control Techniques for a Sustainable World. ed. / H. Selamat ; H. R. H. Ramli ; A. A. M. Faudzi ; R. Z. A. Rahman ; A. J. Ishak ; A. C. Soh ; S. A. Ahmad . New York: Institute of Electrical and Electronics Engineers (IEEE), 2015. 7244794.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A support vector machine based technique for online detection of outliers in transient time series

AU - Martins, Hugo

AU - Palma, Luis

AU - Cardoso, Alberto

AU - Gil, Paulo

N1 - Conference code: 10th

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - WIRELESS SENSOR NETWORKS

KW - ANOMALY DETECTION

U2 - 10.1109/ascc.2015.7244794

DO - 10.1109/ascc.2015.7244794

M3 - Conference contribution

SN - 978-1-4799-7863-2

BT - 2015 10th Asian Control Conference (ASCC)

A2 - Selamat , H.

A2 - Ramli , H. R. H.

A2 - Faudzi , A. A. M.

A2 - Rahman , R. Z. A.

A2 - Ishak , A. J.

A2 - Soh , A. C.

A2 - Ahmad , S. A.

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - New York

T2 - 10th Asian Control Conference (ASCC 2015)

Y2 - 31 May 2015 through 3 June 2015

ER -

Martins H, Palma L, Cardoso A, Gil P. A support vector machine based technique for online detection of outliers in transient time series. In Selamat H, Ramli HRH, Faudzi AAM, Rahman RZA, Ishak AJ, Soh AC, Ahmad SA, editors, 2015 10th Asian Control Conference (ASCC): Emerging Control Techniques for a Sustainable World. New York: Institute of Electrical and Electronics Engineers (IEEE). 2015. 7244794 doi: 10.1109/ascc.2015.7244794

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

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