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‎content/posts/finance/stock_prediction/SARIMA/index.md

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title: "Time Series Analysis and SARIMA Model for Stock Price Prediction"
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date: 2024-07-04T00:00:00+01:00
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description: "Short Stock price analysis on AAPL, then a prediction is tested using SARIMA model"
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menu:
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name: SARIMA
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identifier: SARIMA
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parent: stock_prediction
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weight: 11
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hero: images/test_forecast.png
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tags: ["Finance", "Statistics", "Forecasting"]
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categories: ["Finance"]
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---
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‎content/posts/finance/stock_prediction/SARIMA/sarima_example.ipynb

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<h5 class="card-title">Time Series Analysis and ARIMA Model for Stock Price Prediction</h5>
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<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.
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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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Thursday, July 4, 2024
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| 9 minutes </span>
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href="/posts/finance/stock_prediction/sarima/"
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<description>Recent content in Finance on Stefano Giannini</description>
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<title>Time Series Analysis and ARIMA Model for Stock Price Prediction</title>
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<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.
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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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<title>Time Series Analysis and SARIMA Model for Stock Price Prediction</title>
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<h5 class="card-title">Time Series Analysis and ARIMA Model for Stock Price Prediction</h5>
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<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.
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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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Thursday, July 4, 2024
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| 9 minutes </span>
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class="float-end btn btn-outline-info btn-sm">Read</a>
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<title>Time Series Analysis and ARIMA Model for Stock Price Prediction</title>
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<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.
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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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<h5 class="card-title">Time Series Analysis and ARIMA Model for Stock Price Prediction</h5>
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<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.
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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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Thursday, July 4, 2024
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| 9 minutes </span>
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<a
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href="/posts/finance/stock_prediction/sarima/"
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class="float-end btn btn-outline-info btn-sm">Read</a>
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