The FORECAST Procedure

Overview: FORECAST Procedure

The FORECAST procedure is superseded by newer SAS/ETS procedures that provide more powerful and flexible versions of the forecasting methods provided by PROC FORECAST, in addition to other forecasting methods. Consider one of the following alternatives before using PROC FORECAST:

For forecasting with exponential smoothing or Winters method, consider using the ESM procedure. PROC ESM provides an alternative to using PROC FORECAST with the METHOD=EXPO, METHOD=WINTERS, or METHOD=ADDWINTERS options; it also provides additional forecasting methods that PROC FORECAST does not support. Unlike PROC FORECAST, the ESM procedure optimizes the smoothing weights for the specified forecasting model based on the data. (See Chapter 14: The ESM Procedure, for information about forecasting with PROC ESM.)
For forecasting using time trend models with autoregressive errors, consider using the AUTOREG procedure. PROC AUTOREG provides an alternative to using PROC FORECAST with the METHOD=STEPAR option. (See Chapter 8: The AUTOREG Procedure, for information about PROC AUTOREG.)

If you decide to use PROC FORECAST instead of these newer alternatives, this chapter explains the features of the FORECAST procedure.

The FORECAST procedure provides a quick and automatic way to generate forecasts for many time series in one step. The procedure can forecast hundreds of series at a time, with the series organized into separate variables or across BY groups. PROC FORECAST uses extrapolative forecasting methods where the forecasts for a series are functions only of time and past values of the series, not of other variables.

You can use the following forecasting methods. For each of these methods, you can specify linear, quadratic, or no trend.

The stepwise autoregressive method is used by default. This method combines time trend regression with an autoregressive model and uses a stepwise method to select the lags to use for the autoregressive process.
The exponential smoothing method produces a time trend forecast. However, in fitting the trend, the parameters are allowed to change gradually over time, and earlier observations are given exponentially declining weights. Single, double, and triple exponential smoothing are supported, depending on whether no trend, linear trend, or quadratic trend, respectively, is specified. Holt two-parameter linear exponential smoothing is supported as a special case of the Holt-Winters method without seasons.
The Winters method (also called Holt-Winters) combines a time trend with multiplicative seasonal factors to account for regular seasonal fluctuations in a series. Like the exponential smoothing method, the Winters method allows the parameters to change gradually over time, with earlier observations given exponentially declining weights. You can also specify the additive version of the Winters method, which uses additive instead of multiplicative seasonal factors. When seasonal factors are omitted, the Winters method reduces to the Holt two-parameter version of double exponential smoothing.

The FORECAST procedure writes the forecasts and confidence limits to an output data set. It can also write parameter estimates and fit statistics to an output data set. The FORECAST procedure does not produce printed output.

PROC FORECAST is an extrapolation procedure useful for producing practical results efficiently. However, in the interest of speed, PROC FORECAST uses some shortcuts that cause some statistical results (such as confidence limits) to be only approximate. For many time series, the FORECAST procedure, with appropriately chosen methods and weights, can yield satisfactory results. Other SAS/ETS procedures can produce better forecasts but at greater computational expense.

You can perform the stepwise autoregressive forecasting method with the AUTOREG procedure. You can perform forecasting by exponential smoothing with statistically optimal weights with the ESM procedure. Seasonal ARIMA models can be used for forecasting seasonal series for which the Winters and additive Winters methods might be used.

Additionally, the Time Series Forecasting System can be used to develop forecasting models, estimate the model parameters, evaluate the models’ ability to forecast and display the results graphically. See Chapter 45: Getting Started with Time Series Forecasting, for more details.