The SARIMA model stands as a foundation in the world of time series analysis. An extension of the ARIMA model, SARIMA (Seasonal Autoregressive Integrated Moving Average) brings an added layer of sophistication to forecasting, particularly useful in handling seasonal data.
SARIMA is a statistical model used to predict future points in a time series. It’s particularly adept at handling data with seasonal patterns – like monthly sales data with peaks during holidays, or daily temperatures varying across seasons. The model extends ARIMA by integrating seasonality, making it more versatile.
Components:
The SARIMA model can be understood through its components: Seasonal (S), Autoregressive (AR), Integrated (I), and Moving Average (MA).
- Seasonal: This component models the seasonality in data, capturing regular patterns that repeat over a specific period.
- Autoregressive (AR): This part of the model captures the relationship between an observation and a specified number of lagged observations.
- Integrated (I): Integration involves differencing the time series to make it stationary, a necessary step for many time series models.
- Moving Average (MA): This component models the relationship between an observation and a residual error from a moving average model applied to lagged observations.