Build ARCH and GARCH Models in Time Series using Python | Machine Learning Full Project скачать в хорошем качестве

Build ARCH and GARCH Models in Time Series using Python | Machine Learning Full Project

In this video, we build ARCH and GARCH models in Python to forecast stock market volatility using real financial time series data. This tutorial is designed for absolute beginners, so every concept is explained clearly — from theory to practical coding. We start by understanding what volatility means in financial markets and why it is important for risk management and trading decisions. Then we move step by step through data preprocessing, exploratory data analysis, stationarity testing, and volatility modeling using ARCH and GARCH. By the end of this video, you will clearly understand how volatility behaves in financial time series and how ARCH and GARCH models help in forecasting future market fluctuations. 📌 What you will learn in this project What volatility means in stock markets Difference between ARCH and GARCH models How to preprocess stock market data in Python How to calculate daily returns and rolling volatility How to detect and handle outliers How to test stationarity using the ADF test How to build ARCH and GARCH models How to forecast volatility How to compare model performance using MSE and MAE 🧰 Tools & Libraries Used Python Pandas & NumPy Matplotlib & Seaborn Statsmodels ARCH library Scikit-learn 🎓 Who should watch this video Beginners in Time Series Analysis Students learning Financial Data Science Anyone interested in stock market volatility Python learners working on real-world projects Why this project matters Volatility forecasting is widely used in trading, portfolio management, and risk analysis. Understanding ARCH and GARCH models is a key skill for anyone working with financial time series data. 🔔 Don’t forget to like, share, and subscribe for more AI, Machine Learning, and Data Science projects. Full Project and Dataset - https://www.aionlinecourse.com/ai-pro... #ARCH #GARCH #timeseriesanalysis #stockmarket #volatility #python #datascience #machinelearning #financialmodeling #aiprojects