LearnTeD 2023: Learning from Temporal Data

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align="center"> <h5>LearnTeD 2023: Learning from Temporal Data</h5> <a href="https://dsaa2023.inesctec.pt/">https://dsaa2023.inesctec.pt/</a> <br /> <table border="0" align="center" cellpadding="0" cellspacing="1"> <tr> <td width="150" height="25"><div align="center">Submission Date:</div></td> <td width="150"><div align="center">2023-05-22</div></td> </tr> <tr> <td width="150" height="25"><div align="center">Notification Date:</div></td> <td width="150"><div align="center">2023-07-17</div></td> </tr> <tr> <td width="150" height="25"><div align="center">Conference Date:</div></td> <td width="150"><div align="center">2023-10-09</div></td> </tr> <tr> <td width="150" height="25"><div align="center">Location:</div></td> <td width="150"><div align="center">Thessaloniki, Greece</div></td> </tr> </table> <div class="hidden-phone" align="center"> Viewed: <span class="badge badge-success">2664</span>   Tracked: <span class="badge badge-warning">0</span>   Attend: <span class="badge badge-important">0</span> </div> </div> <br /> </div> </div> <div class="portlet" id="yw1"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-bullhorn"></i> Call For Papers</div> </div> <div class="portlet-content"> <pre>The scope of the special session includes but is not limited to the following: Temporal data clustering Classification and regression of univariate and multivariate time series Early classification of temporal data Deep learning for temporal data Learning representation for temporal data Metric and kernel learning for temporal data Modeling temporal dependencies Time series forecasting Time series annotation, segmentation, and anomaly detection Spatial-temporal statistical analysis Functional data analysis methods Data streams Interpretable/explainable time-series analysis methods Dimensionality reduction, sparsity, algorithmic complexity, and big data challenges Benchmarking and assessment methods for temporal data Applications, including transport, urban computing, weather and climate, ecology, bio-informatics, medical, and energy consumption on temporal data</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2023-05-14</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw3_c0">CCF</th><th id="yw3_c1">CORE</th><th id="yw3_c2">QUALIS</th><th id="yw3_c3">Short</th><th id="yw3_c4">Full Name</th><th id="yw3_c5">Submission</th><th id="yw3_c6">Notification</th><th id="yw3_c7">Conference</th></tr> </thead> <tbody> <tr class="odd"> <td><span class="badge badge-warning">b</span></td><td><span class="badge badge-warning">a*</span></td><td><span class="badge 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