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Wavelet based outlier correction for power controlled turning point detection in surveillance systems Economic Modelling

Li, Yushu LU (2013) In Economic Modelling 30. p.317-321
Abstract
Detection turning points in unimodel has various applications to time series which have cyclic periods. Related techniques are widely explored in the field of statistical surveillance, that is, on-line turning point detection procedures. This paper will first present a power controlled turning point detection method based on the theory of the likelihood ratio test in statistical surveillance. Next we show how outliers will influence the performance of this methodology. Due to the sensitivity of the surveillance system to outliers, we finally present a wavelet multiresolution (MRA) based outlier elimination approach, which can be combined with the on-line turning point detection process and will then alleviate the false alarm problem... (More)
Detection turning points in unimodel has various applications to time series which have cyclic periods. Related techniques are widely explored in the field of statistical surveillance, that is, on-line turning point detection procedures. This paper will first present a power controlled turning point detection method based on the theory of the likelihood ratio test in statistical surveillance. Next we show how outliers will influence the performance of this methodology. Due to the sensitivity of the surveillance system to outliers, we finally present a wavelet multiresolution (MRA) based outlier elimination approach, which can be combined with the on-line turning point detection process and will then alleviate the false alarm problem introduced by the outliers. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to specialist publication or newspaper
publication status
published
subject
keywords
Unimodel, Turning point, Statistical surveillance, Outlier, Wavelet multi-resolution, Threshold.
categories
Popular Science
in
Economic Modelling
volume
30
pages
317 - 321
publisher
Elsevier
external identifiers
  • wos:000315002600036
  • scopus:84867742398
ISSN
0264-9993
DOI
10.1016/j.econmod.2012.08.028
language
English
LU publication?
yes
id
383e9536-7219-4e91-af52-a53b89ee5511 (old id 3044954)
date added to LUP
2016-04-01 09:56:12
date last changed
2022-01-25 18:09:39
@misc{383e9536-7219-4e91-af52-a53b89ee5511,
  abstract     = {{Detection turning points in unimodel has various applications to time series which have cyclic periods. Related techniques are widely explored in the field of statistical surveillance, that is, on-line turning point detection procedures. This paper will first present a power controlled turning point detection method based on the theory of the likelihood ratio test in statistical surveillance. Next we show how outliers will influence the performance of this methodology. Due to the sensitivity of the surveillance system to outliers, we finally present a wavelet multiresolution (MRA) based outlier elimination approach, which can be combined with the on-line turning point detection process and will then alleviate the false alarm problem introduced by the outliers.}},
  author       = {{Li, Yushu}},
  issn         = {{0264-9993}},
  keywords     = {{Unimodel; Turning point; Statistical surveillance; Outlier; Wavelet multi-resolution; Threshold.}},
  language     = {{eng}},
  pages        = {{317--321}},
  publisher    = {{Elsevier}},
  series       = {{Economic Modelling}},
  title        = {{Wavelet based outlier correction for power controlled turning point detection in surveillance systems Economic Modelling}},
  url          = {{http://dx.doi.org/10.1016/j.econmod.2012.08.028}},
  doi          = {{10.1016/j.econmod.2012.08.028}},
  volume       = {{30}},
  year         = {{2013}},
}