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Author: steven-ja

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@@ -35,7 +35,7 @@ Understanding these components is crucial for effective time series modeling.
 
 ### Stationarity
 
-A key concept in time series analysis is stationarity. A stationary time series has constant statistical properties over time, including mean and variance. Many time series models, including ARIMA, assume stationarity. We often need to transform non-stationary data (like most stock price series) to achieve stationarity.
+A key concept in time series analysis is stationarity. A stationary time series has constant statistical properties over time, including mean and variance. Many time series models, including ARIMA, assume stationarity. We often need to transform non-stationary data (like most stock price series) to achieve stationarity. Augmented Dickey-Fuller test can be used to check for stationarity, as showed in next sections.
 
 ## 3. ARIMA Models: Theoretical Background
 

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@@ -489,7 +489,7 @@ <h3 id="components-of-a-time-series">Components of a Time Series</h3>
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 <p>Understanding these components is crucial for effective time series modeling.</p>
 <h3 id="stationarity">Stationarity</h3>
-<p>A key concept in time series analysis is stationarity. A stationary time series has constant statistical properties over time, including mean and variance. Many time series models, including ARIMA, assume stationarity. We often need to transform non-stationary data (like most stock price series) to achieve stationarity.</p>
+<p>A key concept in time series analysis is stationarity. A stationary time series has constant statistical properties over time, including mean and variance. Many time series models, including ARIMA, assume stationarity. We often need to transform non-stationary data (like most stock price series) to achieve stationarity. Augmented Dickey-Fuller test can be used to check for stationarity, as showed in next sections.</p>
 <h2 id="3-arima-models-theoretical-background">3. ARIMA Models: Theoretical Background</h2>
 <p>ARIMA models combine three components:</p>
 <ol>

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         <h5 class="card-title">Time Series Analysis and ARIMA Models for Stock Price Prediction</h5>
-        <p class="card-text post-summary">Time Series Analysis and ARIMA Models for Stock Price Prediction 1. Introduction Time series analysis is a fundamental technique in quantitative finance, particularly for understanding and predicting stock price movements. Among the various time series models, ARIMA (Autoregressive Integrated Moving Average) models have gained popularity due to their flexibility and effectiveness in capturing complex patterns in financial data.
-This article will explore the application of time series analysis and ARIMA models to stock price prediction.</p>
+        <p class="card-text post-summary">1. Introduction Time series analysis is a fundamental technique in quantitative finance, particularly for understanding and predicting stock price movements. Among the various time series models, ARIMA (Autoregressive Integrated Moving Average) models have gained popularity due to their flexibility and effectiveness in capturing complex patterns in financial data.
+This article will explore the application of time series analysis and ARIMA models to stock price prediction. We&rsquo;ll cover the theoretical foundations, practical implementation in Python, and critical considerations for using these models in real-world financial scenarios.</p>
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           Friday, June 28, 2024
-           | 5 minutes </span>
+           | 6 minutes </span>
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       href="/posts/finance/stock_prediction/arima/"
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       <pubDate>Fri, 28 Jun 2024 00:00:00 +0100</pubDate>
 
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-      <description>Time Series Analysis and ARIMA Models for Stock Price Prediction 1. Introduction Time series analysis is a fundamental technique in quantitative finance, particularly for understanding and predicting stock price movements. Among the various time series models, ARIMA (Autoregressive Integrated Moving Average) models have gained popularity due to their flexibility and effectiveness in capturing complex patterns in financial data.
-This article will explore the application of time series analysis and ARIMA models to stock price prediction.</description>
+      <description>1. Introduction Time series analysis is a fundamental technique in quantitative finance, particularly for understanding and predicting stock price movements. Among the various time series models, ARIMA (Autoregressive Integrated Moving Average) models have gained popularity due to their flexibility and effectiveness in capturing complex patterns in financial data.
+This article will explore the application of time series analysis and ARIMA models to stock price prediction. We&amp;rsquo;ll cover the theoretical foundations, practical implementation in Python, and critical considerations for using these models in real-world financial scenarios.</description>
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