Seasonal Autoregressive Integrated Moving Average (SARIMA) is an advanced model used for time series forecasting, building upon the ARIMA framework by incorporating seasonality. SARIMA consists of three main components: seasonal autoregressive (SAR), seasonal differencing (Seasonal I), and seasonal moving average (SMA). This combination allows for the modeling and prediction of time series data that exhibit recurring patterns over specific time intervals. SARIMA is particularly valuable in scenarios where seasonality plays a significant role in shaping data trends, such as in retail sales, climate patterns, or economic indicators. By addressing both non-seasonal and seasonal dynamics, SARIMA enhances the accuracy of forecasts, making it a versatile tool for analysts and researchers across various domains.