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<h5>Description:</h5>
<p>Library for time series distances (e.g. Dynamic Time Warping). The library offers a pure Python implementation and a fast implementation in C. The C implementation has only Cython as a dependency. It is compatible with Numpy and Pandas and implemented to avoid unnecessary data copy operations.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/nl/onderzoek/onderzoeksuitdagingen-2019-2023/ai-driven-data-science"[entry:58:/onderzoek/onderzoeksutidagingen-2019-2023/ai-driven-data-science]data-entry-id="58" target="_blank" rel="noopener">GC1</a> - WP1, WP2</li>
<li><strong>Responsible Research Lead</strong>:<a href="mailto:Jesse.Davis@kuleuven.be" target="_blank" rel="noopener"> Jesse Davis</a>, <a href="mailto:Hendrik.Blockeel@kuleuven.be" target="_blank" rel="noopener">Hendrik Blockeel</a>, <a href="mailto:Luc.DeRaedt@kuleuven.be" target="_blank" rel="noopener">Luc De Raedt</a></li>
<li><strong>Authors</strong>:&nbsp; <a href="mailto:Wannes.Meert@kuleuven.be" target="_blank" rel="noopener">Wannes Meert</a>, <a href="mailto:Aras.Yurtman@kuleuven.be" target="_blank" rel="noopener">Aras Yurtman</a>, <a href="mailto:Kilian.Hendrickx@kuleuven.be" target="_blank" rel="noopener">Kilian Hendrickx</a>, <a href="mailto:Pieter.Robberechts@kuleuven.be" target="_blank" rel="noopener">Pieter Robberechts</a></li>
<li><strong>Programming Language</strong>: Python and C</li>
<li><a href="https://github.com/wannesm/dtaidistance" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories</strong>: Time series, anomaly detection, un/semi-supervised learning</li>
</ul>