Hello - Please excuse for the delay in responding... Yes, forecasting is similar to data mining in a sense that it is a iterative process as well. In your case I would think that you can use PROC ESM to come up with seasonal forecasts, without the need of PROC TIMESERIES. Actually, when you check out the ID statements of both procedures you will find a lot of similar options - for example how to treat missing values. In my opinion you should consider combining TIMESERIES and ESM if you want to modify the original data permanently. Otherwise just ESM should be fine in your case. BTW: did you explore the ODS capabilities of TIMESERIES? It allows you to explore you data graphically without the need of running SGPLOT - unless desired of course. With regards to the seasonality pattern: if you data is let's say daily, then the default seasonal cycle is 7. A daily seasonal model will not be able to pick out the end of month spike. Instead you might want to aggregate your data to weekly level and see if the end of month seasonal cycle gets picked up by the models. Alternatively you might consider seasonal dummies or more advanced transfer models which are available in either ARIMA or UCM. EM 7.1 featues a new experimental node which runs ESM in addition to the existing time series node which runs TIMESERIES. PROC HPF is part of SAS Forecast Server (amongst many other things). Hope this helps. Udo
... View more
