The ARIMA Procedure

PROC ARIMA Statement

PROC ARIMA options;

The following options can be used in the PROC ARIMA statement.

DATA=SAS-data-set

specifies the name of the SAS data set that contains the time series. If different DATA= specifications appear in the PROC ARIMA and IDENTIFY statements, the one in the IDENTIFY statement is used. If the DATA= option is not specified in either the PROC ARIMA or IDENTIFY statement, the most recently created SAS data set is used.

PLOTS<(global-plot-options)> <= plot-request <(options)>> PLOTS<(global-plot-options)> <= (plot-request <(options)> <... plot-request <(options)>>)>

controls the plots produced through ODS Graphics. When you specify only one plot request, you can omit the parentheses around the plot request.

Here are some examples:

plots=none
plots=all
plots(unpack)=series(corr crosscorr)
plots(only)=(series(corr crosscorr) residual(normal smooth))

Global Plot Options:

The global-plot-options apply to all relevant plots generated by the ARIMA procedure. The following global-plot-options are supported:

ONLY: suppresses the default plots. Only the plots specifically requested are produced.
UNPACK: displays each graph separately. (By default, some graphs can appear together in a single panel.)

Specific Plot Options

The following list describes the specific plots and their options.

ALL

produces all plots appropriate for the particular analysis.

NONE

suppresses all plots.

SERIES(<series-plot-options> )

produces plots associated with the identification stage of the modeling. The panel plots corresponding to the CORR and CROSSCORR options are produced by default. The following series-plot-options are available:

ACF

produces the plot of autocorrelations.

ALL

produces all the plots associated with the identification stage.

CORR

produces a panel of plots that are useful in the trend and correlation analysis of the series. The panel consists of the following:

the time series plot
the series-autocorrelation plot
the series-partial-autocorrelation plot
the series-inverse-autocorrelation plot

CROSSCORR

produces panels of cross-correlation plots.

IACF

produces the plot of inverse-autocorrelations.

PACF

produces the plot of partial-autocorrelations.

RESIDUAL(<residual-plot-options> )

produces the residuals plots. The residual correlation and normality diagnostic panels are produced by default. The following residual-plot-options are available:

ACF

produces the plot of residual autocorrelations.

ALL

produces all the residual diagnostics plots appropriate for the particular analysis.

CORR

produces a summary panel of the residual correlation diagnostics that consists of the following:

the residual-autocorrelation plot
the residual-partial-autocorrelation plot
the residual-inverse-autocorrelation plot
a plot of Ljung-Box white-noise test p-values at different lags

HIST

produces the histogram of the residuals.

IACF

produces the plot of residual inverse-autocorrelations.

NORMAL

produces a summary panel of the residual normality diagnostics that consists of the following:

histogram of the residuals
normal quantile plot of the residuals

PACF

produces the plot of residual partial-autocorrelations.

QQ

produces the normal quantile plot of the residuals.

SMOOTH

produces a scatter plot of the residuals against time, which has an overlaid smooth fit.

WN

produces the plot of Ljung-Box white-noise test p-values at different lags.

FORECAST(<forecast-plot-options> )

produces the forecast plots in the forecasting stage. The forecast-only plot that shows the multistep forecasts in the forecast region is produced by default.

The following forecast-plot-options are available:

ALL: produces the forecast-only plot as well as the forecast plot.
FORECAST: produces a plot that shows the one-step-ahead forecasts as well as the multistep-ahead forecasts.
FORECASTONLY: produces a plot that shows only the multistep-ahead forecasts in the forecast region.

OUT=SAS-data-set

specifies a SAS data set to which the forecasts are output. If different OUT= specifications appear in the PROC ARIMA and FORECAST statements, the one in the FORECAST statement is used.