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<h5>Description:</h5>
<p>TSFuse is a Python package for automatically constructing features from multiple time series. Instead of extracting univariate time series features, TSFuse generates new series by applying time series fusion operations.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s):</strong>&nbsp;<a href="/en/research/research-challenges/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></li>
<li><strong>Authors</strong>: <a href="mailto:Arne.DeBrabandere@kuleuven.be" target="_blank" rel="noopener">Arne De Brabandere</a></li>
<li><strong>Programming Language</strong>: Python</li>
<li><a href="https://arnedb.github.io/tsfuse/" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories</strong>: Time series, supervised learning, anomaly &nbsp;detection</li>
</ul>

<h5>Publications:</h5>
<ul>
<li>Arne De Brabandere, Pieter Robberechts, Tim Op De Be&eacute;ck and Jesse Davis. <a href="https://upvedues-my.sharepoint.com/personal/jorallo_upv_edu_es/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fjorallo%5Fupv%5Fedu%5Fes%2FDocuments%2F%5F%5FRESEARCH%2DMISC%2F%5FORGANISED%20%28Ws%2C%20Special%20issues%29%2FAutoDS%40ECML2019%2FFINAL%20PAPERS%2FNO%2DLNCS%2FADS%5F2019%5Fpaper%5F16%2Epdf&amp;parent=%2Fpersonal%2Fjorallo%5Fupv%5Fedu%5Fes%2FDocuments%2F%5F%5FRESEARCH%2DMISC%2F%5FORGANISED%20%28Ws%2C%20Special%20issues%29%2FAutoDS%40ECML2019%2FFINAL%20PAPERS%2FNO%2DLNCS&amp;originalPath=aHR0cHM6Ly91cHZlZHVlcy1teS5zaGFyZXBvaW50LmNvbS86YjovZy9wZXJzb25hbC9qb3JhbGxvX3Vwdl9lZHVfZXMvRVR4eWNHMldobUZCbVZON0NOVzh5S3NCUUh3aGhsemR5ZWdFeDFBbk5lUmEydz9ydGltZT1CLWd3azl4bjJVZw" target="_blank" rel="noopener">Automating Feature Construction for Multi-View Time Series Data</a>. ECML/PKDD Workshop on Automating Data Science 2019.</li>
</ul>

<p>Python package for automatically constructing features from multi-view time series data.</p>