Statistical procedures use ODS Graphics to create graphs as part of their output. ODS Graphics is described in detail in Chapter 21: Statistical Graphics Using ODS in SAS/STAT 13.2 User's Guide.
Before you create graphs, ODS Graphics must be enabled (for example, with the ODS GRAPHICS ON statement). For more information about enabling and disabling ODS Graphics, see the section "Enabling and Disabling ODS Graphics" in that chapter.
The overall appearance of graphs is controlled by ODS styles. Styles and other aspects of using ODS Graphics are discussed in the section "A Primer on ODS Statistical Graphics" in that chapter.
This section provides information about the graphics produced by the ARIMA procedure. (See Chapter 21: Statistical Graphics Using ODS in SAS/STAT 13.2 User's Guide, for more information about ODS statistical graphics.) The main types of plots available are as follows:
plots useful in the trend and correlation analysis of the dependent and input series
plots useful for the residual analysis of an estimated model
forecast plots
You can obtain most plots relevant to the specified model by default. For finer control of the graphics, you can use the PLOTS= option in the PROC ARIMA statement. The following example is a simple illustration of how to use the PLOTS= option.
The series in this example, the monthly airline passenger series, is also discussed later, in Example 7.2.
The following statements specify an ARIMA(0,1,1)(0,1,1) model without a mean term to the logarithms of the airline passengers series, xlog
. Notice the use of the global plot option ONLY
in the PLOTS=
option of the PROC ARIMA statement. It suppresses the production of default graphics and produces only the plots specified
by the subsequent RESIDUAL and FORECAST plot options. The RESIDUAL(SMOOTH)
plot specification produces a time series plot of residuals that has an overlaid loess fit; see Figure 7.21. The FORECAST(FORECAST)
option produces a plot that shows the onestepahead forecasts, as well as the multistepahead forecasts; see Figure 7.22.
proc arima data=seriesg plots(only)=(residual(smooth) forecast(forecasts)); identify var=xlog(1,12); estimate q=(1)(12) noint method=ml; forecast id=date interval=month; run;
PROC ARIMA assigns a name to each graph it creates by using ODS. You can use these names to reference the graphs when you use ODS. The names are listed in Table 7.13.
Table 7.13: ODS Graphics Produced by PROC ARIMA
ODS Graph Name 
Plot Description 
Option 

SeriesPlot 
Time series plot of the dependent series 

SeriesACFPlot 
Autocorrelation plot of the dependent series 

SeriesPACFPlot 
Partialautocorrelation plot of the dependent series 

SeriesIACFPlot 
Inverseautocorrelation plot of the dependent series 

SeriesCorrPanel 
Series trend and correlation analysis panel 
Default 
CrossCorrPanel 
Crosscorrelation plots, either individual or paneled. They are numbered 1, 2, and so on as needed. 
Default 
ResidualACFPlot 
Residualautocorrelation plot 

ResidualPACFPlot 
Residualpartialautocorrelation plot 

ResidualIACFPlot 
Residualinverseautocorrelation plot 

ResidualWNPlot 
Residualwhitenoiseprobability plot 

ResidualHistogram 
Residual histogram 

ResidualQQPlot 
Residual normal QQ Plot 

ResidualPlot 
Time series plot of residuals with a superimposed smoother 
PLOTS= RESIDUAL(SMOOTH) 
ForecastsOnlyPlot 
Time series plot of multistep forecasts 
Default 
ForecastsPlot 
Time series plot of onestepahead as well as multistep forecasts 
PLOTS= FORECAST(FORECAST) 