Stock Market Forecasting with ARIMA Model & Time Series

Stock market chart and analysis

Christ Raharja

Last Updated on September 1, 2023 by GeeksGod

Course : Forecasting Stock Market with ARIMA Model & Time Series

Forecasting Stock Market with ARIMA Model & Time Series: A Comprehensive Guide

The Importance of Stock Market Forecasting

Welcome to the comprehensive project-based course on Forecasting Stock Market with ARIMA Model & Time Series. In this course, we will guide you step by step on how to perform complex analysis and visualization on stock market data. The main focus of this course is to teach you how to forecast future stock prices using the ARIMA model and implement time series.

Course Overview

Before diving into the course content, let’s discuss why learning to forecast the stock market is important and how we can evaluate the accuracy of our forecasts. As investors, it is crucial to make informed decisions based on historical data and patterns. By integrating big data technology into stock market investing, we can identify these patterns and make predictions about the future.

The Role of Accuracy

It is important to note that forecasting the stock market does not guarantee 100% accuracy. However, by analyzing repetitive trends and patterns in historical data, we can increase the likelihood of accurate forecasts. In this course, we will explore various forecasting models and learn how to interpret and apply them to real-world datasets.

Course Objectives

Throughout this course, you can expect to learn the following:

  • The basic fundamentals of stock market forecasting
  • Factors that affect forecasting accuracy
  • Different forecasting models
  • Calculating moving averages
  • Applying the ARIMA model to simple datasets
  • Understanding internal and external factors that impact the stock market
  • Finding and downloading datasets from Kaggle
  • Uploading data to Google Colab Studio
  • Cleaning datasets by removing missing and duplicate values
  • Analyzing and visualizing average highest and lowest stock prices per year
  • Analyzing and visualizing average volume
  • Finding correlation between volume and price changes
  • Calculating 100-day moving averages
  • Analyzing and visualizing volatility
  • Analyzing autocorrelation and partial autocorrelation functions
  • Performing forecasting using the ARIMA model
  • Conducting residual analysis
  • Evaluating forecasting model performance using MAE, MSE, and RMSE

Why Choose this Course?

This course stands out from others as it provides a comprehensive guide on stock market forecasting using the ARIMA model and time series analysis. You will gain hands-on experience by working on real-world datasets and implementing various forecasting techniques. The course also emphasizes the importance of evaluating forecasting model performance, ensuring that you are equipped with the necessary skills to make accurate predictions.

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Stock Market Forecasting: A Lucrative Investment Strategy

The stock market has been a lucrative investment opportunity for centuries. With advancements in technology and the availability of big data, investors can now integrate data-driven approaches into their decision-making process. Forecasting the stock market allows investors to identify patterns and trends in historical data, enabling them to predict future market movements. By enrolling in this course, you will gain valuable insights into stock market forecasting and learn how to apply the ARIMA model and time series analysis techniques to make informed investment decisions.

Learning the Basics: Factors Affecting Stock Market Forecasting Accuracy

Before diving into the intricacies of stock market forecasting, it is essential to understand the fundamentals. This course will provide you with a solid foundation by explaining the factors that affect forecasting accuracy. By examining these factors, you will develop a comprehensive understanding of the various elements that influence stock market predictions.

Calculating Moving Averages: Mastering the Basics

One of the fundamental concepts in stock market forecasting is calculating moving averages. In this course, we will guide you through the step-by-step process of manually calculating moving averages. By mastering this technique, you will be equipped with a powerful tool for predicting future market trends based on historical data.

The ARIMA Model: Unveiling the Mathematics Behind Forecasting

At the heart of stock market forecasting lies the ARIMA model. In this course, we will demystify the mathematics behind this model, ensuring that you understand its underlying principles and functionalities. You will also have the opportunity to apply the ARIMA model to a real-world dataset, gaining practical experience in implementing this powerful forecasting technique.

Internal and External Factors: Determining Stock Market Volatility

Stock markets are influenced by a plethora of internal and external factors. Understanding these factors is crucial for accurate stock market forecasting. In this course, you will delve into the world of market sentiment, earnings reports, interest rates, and other significant variables that impact stock market trends. By analyzing these factors, you will be able to make more precise forecasts and improve your investment decisions.

Realizing the Importance of Data: Finding and Downloading Datasets

Accessing and analyzing relevant data is essential for accurate stock market forecasting. Throughout this course, we will teach you how to find and download datasets from reputable sources like Kaggle. By mastering these techniques, you will gain the ability to explore data independently and expand your forecasting capabilities.

Setting up Google Colab: Your IDE for Stock Market Analysis

To perform data analysis and model implementation effectively, you need a robust integrated development environment (IDE). In this course, we will guide you through the process of setting up Google Colab, an ideal platform for stock market analysis. By utilizing this powerful tool, you will be able to conduct experiments and explore datasets effortlessly.

Project Section: Applying Knowledge to Real-World Scenarios

The project section is the heart of this course. Here, you will put your newfound knowledge and skills to the test by conducting experiments with real-world datasets. Through these hands-on projects, you will gain valuable experience in stock market forecasting and enhance your ability to make accurate predictions.

Evaluating Forecasting Model Performance: Metrics That Matter

Measuring the performance of forecasting models is crucial in determining their effectiveness. In this course, you will learn how to evaluate forecasting model performance using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These metrics will enable you to assess the accuracy and reliability of your forecasts, empowering you to make informed investment decisions.


By enrolling in this comprehensive course on Forecasting Stock Market with ARIMA Model & Time Series, you are taking a significant step towards becoming a skilled stock market forecaster. The knowledge and skills gained throughout this course will equip you with the necessary tools to make informed investment decisions and improve your financial success. So, don’t miss out on this opportunity to enhance your abilities and enroll in this course today!

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What you will learn :

1. Learn basic fundamentals of stock market forecasting, such as getting to know factors that affect the forecasting accuracy and several forecasting models
2. Learn several internal and external factors that can potentially impact stock market
3. Learn how to apply ARIMA (Autoregressive Integrated Moving Average) model into simple dataset and do the basic forecasting
4. Finding correlation between volume & price changes
5. Calculating 100 days moving average
6. Learn how to analyze autocorrelation function & partial autocorrelation function
7. Learn how to perform forecasting using ARIMA model
8. Learn how to perform residual analysis
9. Learn how to do forecasting model evaluation by calculating MAE (Mean Absolute Error), MSE (Mean Squared Error), and RMSE (Root Mean Squared Error)
10. Learn how to clean the dataset by removing missing values and duplicate values

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