Which is the forecast procedure in SAS support?

Which is the forecast procedure in SAS support?

• For forecasting by using time trend models with autoregressive errors, consider using theAUTOREG procedure. The models that can be selected by the PROC FORECAST options METHOD=STEPAR and TREND= can be fit and forecast using PROC AUTOREG, which also allows the inclusion of additional predictor variables in the forecasting model.

How are stepar and Trend selected in Proc forecast?

The models that can be selected by the PROC FORECAST options METHOD=STEPAR and TREND= can be fit and forecast using PROC AUTOREG, which also allows the inclusion of additional predictor variables in the forecasting model. For information about PROC AUTOREG, see Chapter 8, “The AUTOREG Procedure.”

Which is the correct Alpha for Proc forecast?

You should use only two digits for the ALPHA= option because PROC FORECAST rounds the value to the nearest percent (ALPHA=0.101 is the same as ALPHA=0.10). The default is ALPHA=0.05, which produces 95% confidence limits. specifies the maximum order of the autoregressive model. The AR= option is valid only for METHOD=STEPAR.

Which is better for forecasting Proc or UCM?

• For forecasting by using more general and sophisticated time series models, consider using theUCM procedure, which fits and forecasts unobserved components models that are not available in PROC FORECAST. Using UCM models, you can fit and forecast much more complex data patterns than you can by using the simple methods that PROC FORECAST provides.

Can a seasonal ARMA model be used in Proc forecast?

(As a special case, you can use seasonal ARMA models for forecasting seasonal series for which the Winters and additive Winters methods might be used.) PROC ARIMA also provides features for automatically identifying the specific ARIMA model that is most appropriate for the data. ARIMA and ARIMAX models are not available in PROC FORECAST.

What are the three types of proc forecast?

PROC FORECAST can fit three types of time trend models: constant, linear, and quadratic. For other kinds of trend models, other SAS procedures can be used. Exponential smoothing fits a time trend model by using a smoothing scheme in which the weights decline geometrically as you go backward in time.