Regression analysis and time series decomposition serve different purposes in forecasting. While regression focuses on estimating relationships between variables, time series decomposition specifically addresses seasonality and temporal dependencies in data. Time series analysis quantifies how past values influence current outcomes, which regression may struggle to capture effectively. The structured approach of time series allows for a detailed examination of correlations over time, unlike traditional regression methods. Understanding these distinctions is crucial for selecting the appropriate forecasting technique.