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Aplication
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Description
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The autoregressive model is a process where the independent variable is explained by the same variable but delayed one or more periods.
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Moving Average Model is a process where the independent variable is explained by random disturbances that occurred in previous periods.
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The Autoregressive Moving Average Model, is a process where the independent variable is explained by delays and random perturbations of the same variable delayed one or more periods.
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The Autoregressive Integrated Moving Average Model is a process where the independent variable is explained by the same variable delays and random perturbations also delayed, but also the series is differentiated to achieve stationarity. |
| Seasonal Autoregressive Integrated Moving Average Model (p,d,q)(P,D,Q) - SARIMA (p,d,q) (P,D,Q)
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The Model Seasonal Autoregressive Integrated Moving Average, applies to series with periodicity, here the independent variable is described by delays and delayed random perturbations of the same variable, but also the series is differentiated to achieve the stationary and remove seasonality. In this model, the regular order is represented by lowercase and the order seasonal by uppercase.
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