  In a universe with a single currency, there would be no foreign exchange
market, no foreign exchange rates, and no foreign exchange. Over the past
twenty-five years, the way the market has performed those tasks has changed
enormously. The need for intelligent monitoring systems has become a necessity
to keep track of the complex forex market. The vast currency market is a
foreign concept to the average individual. However, once it is broken down into
simple terms, the average individual can begin to understand the foreign
exchange market and use it as a financial instrument for future investing. In
this paper, we attempt to compare the performance of hybrid soft computing and
hard computing techniques to predict the average monthly forex rates one month
ahead. The soft computing models considered are a neural network trained by the
scaled conjugate gradient algorithm and a neuro-fuzzy model implementing a
Takagi-Sugeno fuzzy inference system. We also considered Multivariate Adaptive
Regression Splines (MARS), Classification and Regression Trees (CART) and a
hybrid CART-MARS technique. We considered the exchange rates of Australian
dollar with respect to US dollar, Singapore dollar, New Zealand dollar,
Japanese yen and United Kingdom pounds. The models were trained using 70% of
the data and remaining was used for testing and validation purposes. It is
observed that the proposed hybrid models could predict the forex rates more
accurately than all the techniques when applied individually. Empirical results
also reveal that the hybrid hard computing approach also improved some of our
previous work using a neuro-fuzzy approach.
