SARIMA, or Seasonal Autoregressive Integrated Moving Average, is a statistical model used for time series forecasting. It extends the ARIMA model by incorporating seasonal effects, making it suitable for data that exhibits patterns over specific time intervals, such as monthly or quarterly trends. The model is characterized by parameters that define the autoregressive, differencing, and moving average components, along with seasonal counterparts. The SARIMA model is denoted as SARIMA(p, d, q)(P, D, Q, s), where p, d, and q represent the non-seasonal parameters, while P, D, Q, and s represent the seasonal parameters. This flexibility allows analysts to capture both short-term fluctuations and long-term trends in data, making it a powerful tool for forecasting in various fields, including economics and environmental science.