Learn how to forecast stock market trends with ARIMA (Autoregressive Integrated Moving Average) model & Time Series

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

Requirements
No previous experience in stock market forecasting or time series is required
Willingness to learn and conduct a lot of experiement with the data

Description
Welcome to Forecasting Stock Market with ARIMA Model & Time Series course. This is a comprehensive project based course where you will be guided step by step on how to perform complex analysis and visualisation on stock market data, in addition, the course will be concentrating mainly on forecasting future stock prices using ARIMA model and implementing time series. For the programming language, we are going to use Python alongside several libraries like Pandas for performing data modelling, Numpy for performing complex calculations, and Matplotlib for visualising the data. In the introduction session, you will learn the basic fundamentals of stock market forecasting, such as getting to know factors that affect forecasting accuracy and models that will be used in forecasting. Then, continuing by learning the basic mathematics behind forecasting stock market, you will learn step by step on how to calculate moving averages manually. Not only that, you are also going to learn the mathematics behind the ARIMA model, there will be one comprehensive case study to teach you how to do manual calculation using the ARIMA model. Afterward, you will also learn several internal and external factors that could potentially impact the stock market, for example market sentiment, earning reports, and interest rates. Once you've learnt all necessary knowledge about stock market forecast, we will begin the project, firstly, you will learn how to set up Google Colab since that is the IDE that we are going to use, Then, you will also learn how to find and download stock market datasets from Kaggle. Once everything is all set, you will enter the main section of the course which is the project section where we are going to spend most of our time here, conducting experiments with the dataset. Lastly, at the end of the course, you also learn several metrics for evaluating forecasting model performance, such as Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error, in addition, you will also learn how to implement those metrics on a simple dataset.First of all, before getting into the course, we need to ask ourselves these questions: why should we learn to forecast the stock market? How are we able to know if the forecast is accurate? Well, in my opinion, there are many answers to those questions. Firstly, people have been investing in the stock market since a hundred years ago, therefore, this type of investment has been around for a long time. As the advancement of technology and big data nowadays, people started to realize that integrating big data technology into stock market investing is going to be extremely beneficial as it allows investors to identify patterns from the historical data to make a prediction about the future. Then, the next question might potentially be, how accurate is the forecast going to be? Well, there is no such thing as 100% accuracy. When it comes to forecasting the stock market, we use the data from the past to make a data driven investment decision. Nonetheless, no matter how convinced we are with a pattern from the historical data, there is still no 100% guarantee that the same exact pattern will repeat itself in the future. However, when you spot a repetitive trend or pattern in the data, it basically indicates there is a higher chance that the pattern will happen in the future and that is what the forecasting model is actually based on.Below are things that you can expect to learn from this course:Learn basic fundamentals of stock market forecasting, such as getting to know factors that affect the forecasting accuracy and several forecasting models that will be usedLearn how to calculate moving averageLearn how to apply ARIMA (Autoregressive Integrated Moving Average) model into simple dataset and do the basic forecastingLearn several internal and external factors that can potentially impact stock marketLearn how to find and download datasets from KaggleLearn how to upload data to Goolge Colab StudioLearn how to clean the dataset by removing missing values and duplicate valuesAnalysing & visualising average highest & average lowest stock price per yearAnalysing & visualising average volumeFinding correlation between volume & price changesCalculating 100 days moving averageAnalysing & visualising volatilityLearn how to analyse autocorrelation function & partial autocorrelation functionLearn how to perform forecasting using ARIMA modelLearn how to perform residual analysisLearn how to do forecasting model evaluation by calculating MAE (Mean Absolute Error), MSE (Mean Squared Error), and RMSE (Root Mean Squared Error)

Overview
Section 1: Introduction

Lecture 1 Introduction to the Course

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Introduction to Stock Market Forecasting

Lecture 5 Introduction to Stock Market Forecasting

Section 4: Calculating Moving Average

Lecture 6 Calculating Moving Average

Section 5: ARIMA Model Calculation

Lecture 7 ARIMA Model Calculation

Section 6: Internal & External Factors That Can Impact Stock Market

Lecture 8 Internal & External Factors That Can Impact Stock Market

Section 7: Setting Up Google Colab

Lecture 9 Setting Up Google Colab

Section 8: Finding & Downloading Dataset From Kaggle

Lecture 10 Finding & Downloading Dataset From Kaggle

Section 9: Project: Forecasting Stock Market Trend with ARIMA Model

Lecture 11 Uploading Dataset to Google Colab

Lecture 12 Quick Overview of Stock Market Datset

Lecture 13 Cleaning Dataset by Removing Missing & Duplicate Values

Lecture 14 Analysing & Visualising Average Highest & Lowest Stock Price Per Year

Lecture 15 Analysing & Visualising Average Volume

Lecture 16 Finding Correlation Between Volume & Price Change

Lecture 17 Calculating 100 Days Moving Average

Lecture 18 Analysing & Visualising Volatility

Lecture 19 Auto Correlation Function & Partial Auto Correlation Function

Lecture 20 Forecasting with ARIMA & Performing Residual Analysis

Section 10: Forecasting Model Evaluation

Lecture 21 Calculating Mean Absolute Error, Mean Squared Error & Root Mean Squared Error

Section 11: Conclusion & Summary

Lecture 22 Conclusion & Summary

People who are interested in investing in stock market,People who are interested in learning how to forecast stock market using ARIMA model,People who are interested in learning data analysis & visualisation using Python