Seasonal Autoregressive Integrated Moving Average (SARIMA) is a time series forecasting model designed to predict future values in data exhibiting both non-seasonal and seasonal patterns. Represented as SARIMA(p, d, q)(P, D, Q)s, it encompasses autoregressive, integrated, and moving average components for both non-seasonal and seasonal aspects. SARIMA aims to capture dependencies within the data at different lags and seasonal intervals. Model development involves data exploration, parameter identification, training, and evaluation, often following the Box-Jenkins methodology. The model assumes stationarity or employs differencing to achieve it. Successful application of SARIMA involves careful parameter tuning and consideration of the underlying data’s characteristics, making it a valuable tool for forecasting time series data with recurrent patterns.