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	<h1>Time-Series and Auto-correlation</h1>
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			<span class="post-date">Jun 03</span>

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				Posted by <a href="/forum/user/2259-magandeep-kaur-bedi">Magandeep Kaur Bedi</a>				in <a href="/resources/sigmaway-blog/analytics">Analytics</a>			</span>
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	<p>In the time series, after isolating trends and periodicity, a normalized time series is left. To check whether the data follow some well known stochastic process, model fitting is done. If the model has an autocorrelation then it is de-correlated and after de-correlation it is checked whether it behaves like white noise, or not. The article further explains how to remove auto-correlation in a time series with the help of first order autocorrelation and linear algebra framework in PCA with an example as well. Read full article at: <a href="http://www.datasciencecentral.com/profiles/blogs/how-and-why-decorrelate-time-series" target="_blank">http://www.datasciencecentral.com/profiles/blogs/how-and-why-decorrelate-time-series</a></p>
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	Tags
		<a href="/blog/tags/time-series">
		<span itemprop="keywords">time series</span>
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		<a href="/blog/tags/stochastic-process">
		<span itemprop="keywords">Stochastic process</span>
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		<a href="/blog/tags/auto-correlation">
		<span itemprop="keywords">auto correlation</span>
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		<a href="/blog/tags/linear-algebra-framework">
		<span itemprop="keywords">linear algebra framework</span>
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