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<h2 class="archive-title">2019</h2>

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<h2 class="archive-title">2019</h2>

<article class="archive-item">
<a href="../../gaussian_process_time_series/" class="archive-item-link">PyData Berlin 2019: Gaussian Processes for Timeseries Forecasting</a>
<span class="archive-item-date">
2019-10-10
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<a href="../../gaussian_process_reg/" class="archive-item-link">An Introduction to Gaussian Process Regression</a>
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<pubDate>Thu, 10 Oct 2019 00:00:00 +0000</pubDate>

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<description>In this notebook we run some experiments to demonstrate how we can use Gaussian Processes in the context of time series forecasting. This material is part of a talk on Gaussian Process for Time Series Analysis presented at the PyCon DE &amp;amp; PyData 2019 Conference in Berlin.
Update: Additional material and plots were included for the Second Symposium on Machine Learning and Dynamical Systems at The Fields Institute (virtual event).</description>
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<title>PyData Berlin 2019: Gaussian Processes for Timeseries Forecasting - Dr. Juan Camilo Orduz</title>
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<h1 class="article-title">PyData Berlin 2019: Gaussian Processes for Timeseries Forecasting</h1>
<h1 class="article-title">PyData Berlin 2019: Gaussian Processes for Time Series Forecasting (scikit-learn)</h1>


<span class="article-date">2019-10-10</span>


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<p>In this notebook we run some experiments to demonstrate how we can use Gaussian Processes in the context of time series forecasting. This material is part of a talk on <a href="https://de.pycon.org/program/pydata-knlnbb-gaussian-process-for-time-series-analysis-dr-juan-orduz/">Gaussian Process for Time Series Analysis</a> presented at the <a href="https://de.pycon.org/">PyCon DE &amp; PyData 2019 Conference</a> in Berlin.</p>
<p>In this notebook we run some experiments to demonstrate how we can use Gaussian Processes in the context of time series forecasting with <a href="https://scikit-learn.org/stable/modules/gaussian_process.html">scikit-learn</a>. This material is part of a talk on <a href="https://de.pycon.org/program/pydata-knlnbb-gaussian-process-for-time-series-analysis-dr-juan-orduz/">Gaussian Process for Time Series Analysis</a> presented at the <a href="https://de.pycon.org/">PyCon DE &amp; PyData 2019 Conference</a> in Berlin.</p>
<p><strong>Update:</strong> Additional material and plots were included for the <a href="http://www.fields.utoronto.ca/activities/20-21/dynamical">Second Symposium on Machine Learning and Dynamical Systems</a> at <a href="http://www.fields.utoronto.ca/">The Fields Institute</a> (virtual event).</p>
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