The SARIMA (Seasonal Autoregressive Integrated Moving Average) model, an extension of the ARIMA model, is a foundational tool in time series analysis, particularly adept at handling data with seasonal patterns. Comprising Seasonal (S), Autoregressive (AR), Integrated (I), and Moving Average (MA) components, SARIMA captures the seasonality in data, models the relationship between observations and lagged values, integrates differencing for stationarity, and accounts for residual errors from moving averages. The seasonal component is vital for capturing recurring patterns, while the autoregressive component accounts for lagged correlations, the integrated component involves differencing for stationarity, and the moving average component captures short-term fluctuations. The synergy of these components, with their respective orders denoted by parameters like p, d, q, P, D, Q, and m, enables SARIMA’s versatility in predicting future points in time series data, making it especially useful for forecasting in scenarios with seasonal variations. Selecting appropriate orders is crucial in fitting SARIMA models, often guided by autocorrelation and partial autocorrelation plots and an understanding of the data’s seasonal characteristics. Overall, SARIMA serves as a sophisticated and effective tool for time series forecasting and pattern analysis.