Why? With 60 months of data, why not forecast out at least one cycle(12)? The default is NOT to search and adjust for outliers. The default is to forecast out 3 periods. If we pretend that the system is a black box and just focused on the forecast, most would visually say that it looks ok, but what happens if we dig deeper and consider the model that was built? Using simple and easy data avoids the difficult process of admitting you might not able complicated data. The broadband example has the raw data and we will use that as we can benchmark it.

On the flipside, a user may think there is no seasonality in there data when there actually is so let's take the humans out of the equation. The ACF/PACF statistics helps algorithms(and people) to help identify seasonality. The tool should be able to identify seasonality or if there is none to be found. You shouldn't need to prescreen your data. I think this is just trying to avoid bad answers and under the guise of it being 'faster'. They then tell you that it will run faster if you know there is no seasonality. The online tutorial section called 'Examining the data' talks about how Modeler can find the best seasonal models or nonseasonal models. This makes us wonder why there is no challenging data to stress the system here?įor series 4 and 5 both are find to have seasonality. The 5 examples have no variability and would be categorized as 'easy' to model and forecast with no visible outliers. We went through the first 5 'broadband' examples that come with the trial that are set to run by default. There are 2 sets of time series examples included with the 30 day trial. We would be glad to hear any opinions(as always) differing or adding to ours.

We tested it and have more questions than answers. IBM released version SPSS Modeler 18 recently and with it a 30 day trial version.

Spss modeler 18 iuanywhere