Wavelet based outlier correction for power controlled turning point detection in surveillance systems Economic Modelling
(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:
https://lup.lub.lu.se/record/3044954
- author
- Li, Yushu LU
- organization
- publishing date
- 2013
- 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}}, }