The ARIMA Procedure
- Overview
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Getting Started
The Three Stages of ARIMA Modeling Identification Stage Estimation and Diagnostic Checking Stage Forecasting Stage Using ARIMA Procedure Statements General Notation for ARIMA Models Stationarity Differencing Subset, Seasonal, and Factored ARMA Models Input Variables and Regression with ARMA Errors Intervention Models and Interrupted Time Series Rational Transfer Functions and Distributed Lag Models Forecasting with Input Variables Data Requirements -
Syntax
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Details
The Inverse Autocorrelation Function The Partial Autocorrelation Function The Cross-Correlation Function The ESACF Method The MINIC Method The SCAN Method Stationarity Tests Prewhitening Identifying Transfer Function Models Missing Values and Autocorrelations Estimation Details Specifying Inputs and Transfer Functions Initial Values Stationarity and Invertibility Naming of Model Parameters Missing Values and Estimation and Forecasting Forecasting Details Forecasting Log Transformed Data Specifying Series Periodicity Detecting Outliers OUT= Data Set OUTCOV= Data Set OUTEST= Data Set OUTMODEL= SAS Data Set OUTSTAT= Data Set Printed Output ODS Table Names Statistical Graphics
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Examples
- References
| PROC ARIMA Statement |
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)>>)>
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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
breaks a graphic that is otherwise paneled into individual component plots.
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> )
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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
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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> )
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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
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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
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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.
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> )
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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.