A MEP and IP Based Flexible Neural Tree Model for Exchange Rate Forecasting

Abstract

Forecasting exchange rate is an important financial problem that is received much more attentions because of its difficulty and practical applications. The problem of prediction of foreign exchange rates by using multi expression programming and immune programming based flexible neural tree (MEPIP-FNT) is presented in this paper. This work is an extension of our previously traditional FNT model which can optimize the architectures and the weights of flexible neuron model respectively. The novel MEPIPFNT model with the underlying immune theories is capable of evolving the architectures and the weights simultaneously. To demonstrate the efficiency of the model, we conduct three different datasets in our forecasting performance analysis.