Developmental errors in aggregate trending models
As discussed previously, while model overlays are typically most useful for analyzing the growth pattern and potential strategies of specific businesses or within more localized market segments, aggregate trending models tend to be most useful for identifying potential model anomolies within related market sectors and predicting the likely future existence of breakthroughs that could lead to rapid acquisition of market share.
The downside is it is still nearly impossible to predict WHAT those breakthroughs will be other than by using relatively new analyis techniques.
And that’s still a big problem. In order to undertake proper development of models to analyze aggregate trending models, multiple output scenarios have to be posited and “assumed” to some degree, but other than comparing the model results to real world results it is for the most part very difficult to accelerate the detection of predictive differences off the posited models.