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A piecewise linear representation based on compression ratio

  • Beihang University
  • China Aerospace Science and Technology Corporation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As an important method of data preprocessing, some algorithms about piecewise linear representation have been proposed. However, one or more threshold parameters must be input firstly in these algorithms. It is hard to determine these parameters because different time series have different characteristics. So the users have to try and test for many times. Comparing with the existing effective algorithms, the proposed algorithm in this paper only need the compression ratio and then it selects the segmentation points by two iterations. In the first iteration, it selects all the extreme points. Then in the second iteration, it abandons or adds the segmentation points based on the compression ratio and the number of extreme points. The criterion in the second iteration is the vertical distance. At last, two time series are used to verify the effectiveness of the proposed algorithm and the results show that the fitting error of the proposed algorithm is smaller than that of the existing SEEP algorithm and the algorithm based on important points.

Original languageEnglish
Title of host publicationProceedings of 2015 Prognostics and System Health Management Conference, PHM 2015
EditorsTingdi Zhao, Michael G. Pecht, Shunong Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467385534
DOIs
StatePublished - 12 Jan 2016
EventPrognostics and System Health Management Conference, PHM 2015 - Beijing, China
Duration: 21 Oct 201523 Oct 2015

Publication series

NameProceedings of 2015 Prognostics and System Health Management Conference, PHM 2015

Conference

ConferencePrognostics and System Health Management Conference, PHM 2015
Country/TerritoryChina
CityBeijing
Period21/10/1523/10/15

Keywords

  • compression ratio
  • iteration
  • piecewise linear representation
  • time series
  • vertical distance

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