ARIMA Structure

Specify the values of the various non-seasonal and seasonal components of the ARIMA model. In each case, set the operator to = (equal to) or <= (less than or equal to), then specify the value in the adjacent field. Values must be non-negative integers specifying the degrees.

Non seasonal. The values for the various nonseasonal components of the model.

• Degrees of autocorrelation (p). The number of autoregressive orders in the model. Autoregressive orders specify which previous values from the series are used to predict current values. For example, an autoregressive order of 2 specifies that the value of the series two time periods in the past be used to predict the current value.
• Derivation (d). Specifies the order of differencing applied to the series before estimating models. Differencing is necessary when trends are present (series with trends are typically nonstationary and ARIMA modeling assumes stationarity) and is used to remove their effect. The order of differencing corresponds to the degree of series trend--first-order differencing accounts for linear trends, second-order differencing accounts for quadratic trends, and so on.
• Moving average (q). The number of moving average orders in the model. Moving average orders specify how deviations from the series mean for previous values are used to predict current values. For example, moving-average orders of 1 and 2 specify that deviations from the mean value of the series from each of the last two time periods be considered when predicting current values of the series.

Seasonal. Seasonal autocorrelation (SP), derivation (SD), and moving average (SQ) components play the same roles as their nonseasonal counterparts. For seasonal orders, however, current series values are affected by previous series values separated by one or more seasonal periods. For example, for monthly data (seasonal period of 12), a seasonal order of 1 means that the current series value is affected by the series value 12 periods prior to the current one. A seasonal order of 1, for monthly data, is then the same as specifying a nonseasonal order of 12.

The seasonal settings are considered only if seasonality is detected in the data, or if you specify Period settings on the Advanced tab.