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Accurate Changing Point Detection for l(1) Mean Filtering
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-1593-7520
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-1927-1690
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0003-0355-2663
2016 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 23, no 2, 297-301 p.Article in journal (Refereed) PublishedText
Abstract [en]

It is often desirable to find the underlying trends in time series data. This is a well known signal processing problem that has many applications in areas such as financial data analysis, climatology, biological and medical sciences. Mean filtering finds a piece-wise constant trend in the data while trend filtering finds a piece-wise linear trend. When the signal is noisy, the main difficulty is finding the changing points in the data that mark the transition points when the mean or the trend changes. Previously proposed methods based on l(1) filtering suffer from the occurrence of false changing points in the estimate. This is known as the staircase effect. The main contribution in this paper is incorporating a technique to remove these false changing points to a fast mean filtering algorithm, referred to as the taut-string method, resulting in an efficient procedure with accurate change point detection and thus the removal of the stair-case effect.

Place, publisher, year, edition, pages
2016. Vol. 23, no 2, 297-301 p.
Keyword [en]
l(1) mean filtering, stair-case effect, taut-string algorithm
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-186015DOI: 10.1109/LSP.2016.2517605ISI: 000373742000009ScopusID: 2-s2.0-84962280942OAI: oai:DiVA.org:kth-186015DiVA: diva2:926691
Note

QC 20160509

Available from: 2016-05-09 Created: 2016-04-29 Last updated: 2016-05-09Bibliographically approved

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Ottersten, JohanWahlberg, BoRojas, Cristian R.
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