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	<title>ARIMA &#8211; Howard Nguyen</title>
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	<description>Ph.D. in Data Science</description>
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		<title>Autoregressive Integrated Moving Average &#8211; ARIMA</title>
		<link>https://howardnguyen.com/autoregressive-integrated-moving-average-arima/</link>
		
		<dc:creator><![CDATA[admin]]></dc:creator>
		<pubDate>Fri, 20 Oct 2023 00:30:47 +0000</pubDate>
				<category><![CDATA[ARIMA]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[AutoRegression]]></category>
		<category><![CDATA[Autoregressive Integrated Moving Average]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Time Series]]></category>
		<category><![CDATA[AutoRegression in Time Series]]></category>
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					<description><![CDATA[Autoregression, often abbreviated as AR, is a fundamental concept in time series analysis and forecasting. It's a model that relates a variable to its own past values. Autoregressive models are used to capture and represent temporal dependencies within a time series data.]]></description>
										<content:encoded><![CDATA[<p>The Autoregressive Integrated Moving Average (ARIMA) model is a widely used time series forecasting model that combines autoregression (AR), differencing (I for Integrated), and moving averages (MA) to capture various aspects of time series data. ARIMA is effective for modeling time series with trend and seasonality components. Here&rsquo;s an overview of the components and structure of the ARIMA&#8230;</p>
<p><a href="https://howardnguyen.com/autoregressive-integrated-moving-average-arima/" rel="nofollow">Source</a></p>]]></content:encoded>
					
		
		
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