<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>3853</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>418</pageFirst>
    <pageLast>424</pageLast>
    <pageNumber/>
    <edition/>
    <issue>July</issue>
    <volume>125</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-05-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Revisiting inaccuracies of time series averaging under dynamic time warping</title>
    <abstract language="eng">This article revisits an analysis on (in)accuracies of time series averaging under dynamic time warping (dtw) conducted by Niennattrakul and Ratanamahatana [16]. They proposed a correctness-criterion for dtw-averages and postulated that dtw-averages can drift out of the cluster of time series to be averaged. They claimed that dtw-averages are inaccurate if they violate the correctness-criterion or suffer from the drift-out phenomenon. Furthermore, they conjectured that such inaccuracies are caused by the lack of triangle inequality. In this article, we show that a rectified version of the correctness-criterion is unsatisfiable and that the concept of drift-out is geometrically and operationally inconclusive. Satisfying the triangle inequality is insufficient to achieve correctness and unnecessary to overcome the drift-out phenomenon. We place the concept of drift-out on a principled basis and show that Fréchet means never drift out. The adjusted drift-out is a way to test to which extent an approximated dtw-average is coherent. Empirical results show that approximations obtained by the state-of-the-art averaging methods are incoherent in over a third of all cases.</abstract>
    <parentTitle language="eng">Pattern Recognition Letters</parentTitle>
    <identifier type="doi">10.1016/j.patrec.2019.05.022</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Brijnesh Jain</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>k-means</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Time series averaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dynamic time warping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Time series</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
  </doc>
</export-example>
