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<p class="MsoNormal"><span lang="EN-US">You can use it to get a single similarity / closeness number between two timeseries and then feed that into a clustering algorithm.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">For instance look at<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><a href="https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping">https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping</a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">as a first idea:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">if you expand the distance function d = lambda x,y: abs(x-y) to a multivariate local distance<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">d2 = lambda a,b: np.sqrt(float((a[0]-b[0])**2 + (a[1]-b[1])**2)
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">(or any other n-dim metric)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Then you have an algorithm that could cluster the timeseries.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">It does also work when the timeseries are of equal length…<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Best<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Mikkel Brynildsen<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">From:</span></b><span lang="EN-US"> scikit-learn <scikit-learn-bounces+mbrynildsen=grundfos.com@python.org>
<b>On Behalf Of </b>lampahome<br>
<b>Sent:</b> 17. januar 2019 08:45<br>
<b>To:</b> Scikit-learn mailing list <scikit-learn@python.org><br>
<b>Subject:</b> Re: [scikit-learn] Any clustering algo to cluster multiple timing series data?<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">Mikkel Haggren Brynildsen <</span><a href="mailto:mbrynildsen@grundfos.com"><span lang="EN-US">mbrynildsen@grundfos.com</span></a><span lang="EN-US">>
</span><span lang="JA" style="font-family:"Yu Gothic",sans-serif">於</span><span lang="EN-US"> 2019</span><span lang="JA" style="font-family:"Yu Gothic",sans-serif">年</span><span lang="EN-US">1</span><span lang="JA" style="font-family:"Yu Gothic",sans-serif">月</span><span lang="EN-US">17</span><span lang="JA" style="font-family:"Yu Gothic",sans-serif">日</span><span lang="JA">
</span><span lang="JA" style="font-family:"Yu Gothic",sans-serif">週四</span><span lang="JA">
</span><span lang="JA" style="font-family:"Yu Gothic",sans-serif">下午</span><span lang="EN-US">3:07</span><span lang="JA" style="font-family:"Yu Gothic",sans-serif">寫道</span><span lang="JA" style="font-family:"Yu Gothic",sans-serif">：</span><span lang="EN-US"><o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">What about dynamic time warping ?<o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">I thought DTW is used to different length of two datasets<o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">But I only get the same length of two datasets.<o:p></o:p></span></p>
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<p class="MsoNormal">Maybe it doesn't work?<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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