The ESM procedure generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data.
For typical time series, you can use the following smoothing models:
simple
double
linear
damped trend
seasonal
Winters method (additive and multiplicative)
Additionally, transformed versions of these models are provided:
log
square root
logistic
Box-Cox
Graphics are available with the ESM procedure. For more information, see the section ODS Graphics.
The exponential smoothing models supported in PROC ESM differ from those supported in PROC FORECAST since all parameters associated with the forecasting model are optimized by PROC ESM based on the data.
The ESM procedure writes the time series extrapolated by the forecasts, the series summary statistics, the forecasts and confidence limits, the parameter estimates, and the fit statistics to output data sets. The ESM procedure optionally produces printed output for these results by using the Output Delivery System (ODS).
The ESM procedure can forecast both time series data, whose observations are equally spaced by a specific time interval (for example, monthly, weekly), or transactional data, whose observations are not spaced with respect to any particular time interval. Internet, inventory, sales, and similar data are typical examples of transactional data. For transactional data, the data are accumulated based on a specified time interval to form a time series prior to modeling and forecasting.