What is the difference between ARIMA and SARIMA models?

SARIMA extends ARIMA by explicitly modeling seasonal patterns in time series.

ARIMA captures autoregression, differencing, and moving averages but does not handle seasonality. SARIMA introduces seasonal components (P, D, Q, s) to model repeating patterns at fixed intervals. This makes SARIMA suitable for data with strong seasonal cycles like monthly or weekly patterns.