The FORECAST Procedure
- Overview
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Getting Started
Giving Dates to Forecast Values Computing Confidence Limits Form of the OUT= Data Set Plotting Forecasts Plotting Residuals Model Parameters and Goodness-of-Fit Statistics Controlling the Forecasting Method Introduction to Forecasting Methods Time Trend Models Time Series Methods Combining Time Trend with Autoregressive Models -
Syntax
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Details
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Examples
- References
| Data Requirements |
You should have ample data for the series that you forecast by using PROC FORECAST. However, the results might be poor unless you have a good deal more than the minimum amount of data the procedure allows. The minimum number of observations required for the different methods is as follows:
If METHOD=STEPAR is used, the minimum number of nonmissing observations required for each series forecast is the TREND= option value plus the value of the NLAGS= option. For example, using NLAGS=13 and TREND=2, at least 15 nonmissing observations are needed.
If METHOD=EXPO is used, the minimum is the TREND= option value.
If METHOD=WINTERS or ADDWINTERS is used, the minimum number of observations is either the number of observations in a complete seasonal cycle or the TREND= option value, whichever is greater. (However, there should be data for several complete seasonal cycles, or the seasonal factor estimates might be poor.) For example, for the seasonal specifications SEASONS=MONTH, SEASONS=(QTR DAY), or SEASONS=(MONTH DAY HOUR), the longest cycle length is one year, so at least one year of data is required. At least two years of data is recommended.