Limitations of Using Rules in Forecasting Currency Echange Rates
Martin Rat · 2000
fizzy neural networks have a growing importance for problems where one wants to avoid pure black box solutions or when hints - i.e. known dependencies (rules) - are used. In this paper an application to the field of currency forecasting is examined. It is shown that a classical set of rules does not lead to an acceptable solution and a classical neural network does apply better. The latter is only basing on the tame series data which is fed into the network without rules. The example used here is a medium term forecasting of a one month interval. The subject is the Deutschmark/US-Dollar exchange rate over a period of several years.