I have been asked several times, "How can RBF help me in my business?" A simplistic view of RBF is that RBF is an amalgamation of statistics, rules and data curve matching. By adjusting certain parameters of the curve matching using well-defined and proven-over-time methods and procedures, each iteration of the rules (rules always run over and over) should yield closer and closer match to the real data so that the forecast data are far more correct. Basically, the rules help the forecaster in giving the forecaster a highly accurate initial forecast. The forecaster, of course, can always change some of the parameters of the rules so that he/she can see the difference in what would happen. What a great training tool for forecasters!!
OK, that sales pitch being said, what about some examples? Hopefully, next year at Rules Fest, we will be able to show the rules working on real data (hopefully some massive data sets) and show how changing one small parameter can have drastic (good or bad) effects on the outcome. In the meantime, just drop me a line about what you and/or your company is doing in the field of forecasting. Are you using Linear Regression, Multiple Linear Regression, Box Jenkins, Neural Networks, Econometric Forecasting or what? Sometimes a smaller (or even some rather large) companies don not use ANY software to help with this complex problem. This is what we call, "Flying by the seat of your pants." solution. That SOP solution can get a company burned badly. However, relying on poor data or insufficient data can get you into hot water as well.
For example, if you are using monthly data, you need (OK, should have) at least 5 years (60 months) of really clean data from various internal and/or external sources to give the RBF a chance of accuracy. Other systems that use cycles of yearly data or non-standard cycles, are tougher but a decent RBF should be able to handle that in the curve matching routines and, again, if the system has sufficient data then the forecasting tool will have a much better chance of fitting the forecast to the provided data.
Hopefully, I'll have some more on this next week.