The GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is used in time series analysis to forecast volatility by capturing the changing variance over time, which is crucial for understanding financial market behavior. It extends the ARCH model by incorporating lagged terms of both the squared residuals and the conditional variance, allowing for more flexibility and accuracy in modeling time-dependent volatility patterns.