Volatility is a statistical measure of dispersion of returns for a given security or market index. Understanding volatility in time series is crucial for several reasons, especially in fields like finance, economics, control systems, and signal processing. Understanding volatility in finance is crucial because it acts as a bridge between uncertainty and decision-making. It helps investors quantify, price, and manage risk, making it central to everything from portfolio construction and option pricing to financial regulation and macroeconomic policy. In this chapter, we will discuss volatility and how to model it using both traditional statistical approaches and modern machine learning techniques (with an emphasis on traditional statistical approaches), exploring methods that capture its dynamic nature and improve forecasting accuracy.