In recent years, car options like air-conditioning, automatic
gears, car stereo, power windows, sunroof etc. were getting more
and more important for car manufacturers. Especially at those
car manufacturers which offer cars individually according to
customer requirements, the options affect 30% - 40% of the parts
by now. Therefore, very detailed planning and forecasting of
options becomes more and more important. Because customer
behavior concerning the options varies from model to model and
from country to country, it seems necessary and reasonable to
forecast each option for each model and each country separately.
The resulting huge number of data sets requires an automatic
forecasting tool that adapts itself to the actual data sets and
that requires almost no user interaction. Because depending on
the characteristics of a time series the quality of the
forecasting results varies a lot, and because "N heads are
better than one," the basic idea is m select in a first step the
most appropriate forecasting pro