In the field of time series analysis, the SARIMA model serves as a foundation. SARIMA (Seasonal Autoregressive Integrated Moving Average), an extension of the ARIMA model, adds another level of complexity to forecasting and is especially helpful when handling seasonal data.
A statistical model called SARIMA forecasts subsequent points in a time series. It excels at processing data with seasonal patterns, such as monthly sales data that peaks around holidays or daily temperature variations from season to season. By incorporating seasonality, the model expands on ARIMA and gains greater adaptability.
Parts:
The components of the SARIMA model are as follows: moving average (MA), autoregressive (AR), integrated (I), and seasonal (S).
- Seasonal: This element captures recurring patterns that recur over a given period and models the seasonality in the data.
- The model’s autoregressive (AR) component describes how an observation and a certain number of lag observations are related to each other.
- Integrated (I): For many time series models, it is essential to differentiate the time series to make it stationary.
- Moving Average (MA): When a moving average model is applied to lagged observations, this component simulates the relationship between an observation and a residual error.