Seasonal Autoregressive Integrated Moving Average, or SARIMA, is a time series forecasting method used to model and predict data that exhibits seasonal patterns or periodic fluctuations. It’s an extension of the ARIMA model that includes seasonality.
SARIMA models account for:
- Seasonal Patterns: Capturing repetitive patterns over fixed intervals of time.
- Autoregressive (AR) Components: Dependent relationships between an observation and a number of lagged observations.
- Differencing (I): Transforming a time series to achieve stationarity by computing differences between consecutive observations.
- Moving Average (MA) Components: Modeling the dependency between an observation and a residual error from a moving average model applied to lagged observations.
By combining these components with their seasonal counterparts, SARIMA models can forecast time series data, taking into account both non-seasonal and seasonal patterns. They are particularly useful for data with complex seasonal trends and can provide accurate predictions for such time series.