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ACCUMULATE=option
specifies how to accumulate the data table observations within each time period for the variables in the variable-list. If the ACCUMULATE= option is not specified in the FORECAST statement, accumulation is determined by the ACCUMULATE= option in the ID statement. Use the ACCUMULATE= option with multiple FORECAST statements when you want different accumulation specifications for different variables. For more information, see the ACCUMULATE= option in the ID statement.
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ALPHA=number
specifies the significance level to use in computing the confidence limits of the forecast, where number must be between 0 and 1. By default, ALPHA=0.05, which produces 95% confidence intervals.
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BACK=n
specifies the number of observations before the end of the data where the multistep forecasts are to begin. By default, BACK=0.
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CRITERION=criterion
specifies the model selection criterion (statistic of fit) to use to select from several candidate models. For a list of valid values, see the CRITERION= option in the HPFDIAGNOSE procedure in SAS Forecast Server Procedures: User's Guide. By default, CRITERION=RMSE.
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LEAD=n
specifies the number of periods ahead to forecast (the forecast lead or horizon).
The value n is not relative to the last nonmissing observation of a particular series, but is instead relative to the BACK= option specification to the last observation in the input data table or the accumulated series. Thus, if a series has missing values at the end, the actual number of forecasts computed for that series is greater than n.
By default, LEAD=0.
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MEDIAN
estimates the median forecast values and uses those values for forecasting. (By default, PROC CESM uses mean values for forecasting.) If you do not specify the TRANSFORM= option, no transformation is applied to the time series, so the mean and median forecast values are identical.
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METHOD=model-name
specifies the forecasting model to use to forecast the time series. You can specify the following forecasting model-names:
- ADDWINTERS
requests the Winters additive method.
- BEST
requests the best candidate smoothing model among the SIMPLE, LINEAR, DAMPTREND, SEASONAL, ADDWINTERS, or WINTERS methods.
- BESTN
requests the best candidate nonseasonal smoothing model among the SIMPLE, LINEAR, or DAMPTREND methods.
- BESTS
requests the best candidate seasonal smoothing model among the SEASONAL, ADDWINTERS, or WINTERS methods.
- DAMPTREND
requests damped trend exponential smoothing.
- DOUBLE
requests second-order exponential smoothing.
- LINEAR
requests linear (Holt) exponential smoothing.
- MULTSEASONAL
requests multiplicative seasonal exponential smoothing.
- SEASONAL
requests additive seasonal exponential smoothing.
- SIMPLE
requests simple (single) exponential smoothing.
- WINTERS
requests Winters multiplicative method.
By default, METHOD=BEST.
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SETMISSING=option | number
specifies how to assign missing values (either input or accumulated) in the accumulated time series for variables in the variable-list. If the SETMISSING= option is not specified in the FORECAST statement, missing values are set according to the value of the SETMISSING= option in the ID statement. For more information, see the SETMISSING= option in the ID statement.
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TRANSFORM=option
specifies the time series transformation to be applied to the input or accumulated time series. You can specify the following values for option:
- AUTO
automatically chooses between NONE and LOG on the basis of the model selection criteria.
- BOXCOX(n)
performs Box-Cox transformation with parameter number (n), where n must be between –5 and 5.
- LOG
performs logarithmic transformation.
- LOGISTIC
performs logistic transformation.
- NONE
performs no transformation.
- SQRT
performs square-root transformation.
By default, TRANSFORM=NONE.
When the TRANSFORM= option is specified, the time series must be strictly positive. After the time series is transformed, the model parameters are estimated by using the transformed series. The forecasts of the transformed series are then computed, and finally the transformed series forecasts are inverse-transformed. The inverse transform produces either mean or median forecasts depending on whether the MEDIAN option is specified. For more information, see the sections Transformations and Inverse Transformations.