Implementing SARIMA involves several steps: 1. Data Preparation: The data should be stationary. This may require differencing the data or applying transformations. 2. Model Identification: Determine the order of the model (p, d, q, P, D, Q, s) using tools like ACF and PACF plots. 3. Parameter Estimation: Fit the model using statistical software like R or Python. 4. Model Validation: Evaluate the model using diagnostic tests to ensure it adequately captures the data patterns. 5. Forecasting: Use the model to make predictions.
