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SAS/ETS 9.22 User's Guide 77

SAS/Ets 9.22 User's Guide 77. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 752 F Chapter 13 The ESM Procedure Output 13.1.1 Retail Sales Forecast Plots The default simple exponential smoothing model is used because the MODEL option is omitted on the FORECAST statement. Note that for simple exponential smoothing the forecasts are constant. The following ESM procedure statements are identical to the preceding statements except that the PRINT FORECASTS option is specified proc esm data sales out nextyear print forecasts id date interval month forecast _numeric_ run In addition to forecasting each of the monthly time series the preceding statements print the forecasts by using the Output Delivery System ODS the forecasts are partially shown in Output 13.1.2. This output shows the predictions prediction standard errors and the upper and lower confidence limits for the next twelve monthly periods. Example 13.2 Forecasting of Transactional Data F 753 Output 13.1.2 Forecast Tables Shoe Department Sales Obs Time The ESM Procedure Forecasts for Variable shoes Standard Forecasts Error 95 Confidence Limits 62 FEB1999 6009.1986 1069.4059 3913.2016 8105.1956 63 MAR1999 6009.1986 1075.7846 3900.6996 8117.6976 64 APR1999 6009.1986 1082.1257 3888.2713 8130.1259 65 MAY1999 6009.1986 1088.4298 3875.9154 8142.4818 66 JUN1999 6009.1986 1094.6976 3863.6306 8154.7666 67 JUL1999 6009.1986 1100.9298 3851.4158 8166.9814 68 AUG1999 6009.1986 1107.1269 3839.2698 8179.1274 69 SEP1999 6009.1986 1113.2895 3827.1914 8191.2058 70 OCT1999 6009.1986 1119.4181 3815.1794 8203.2178 71 NOV1999 6009.1986 1125.5134 3803.2329 8215.1643 72 DEC1999 6009.1986 1131.5758 3791.3507 8227.0465 73 JAN2000 6009.1986 1137.6060 3779.5318 8238.8654 Example 13.2 Forecasting of Transactional Data This example illustrates how the ESM procedure can be used to forecast transactional data. The following DATA step creates a data set from data recorded at several Internet Web sites. The data set WEBSITES contains a variable TIME that represents time and the variables ENGINE BOATS CARS and PLANES that