Abstract
The complex nature of the foreign exchange (FOREX) market along with the increased interest towards the currency exchange market has prompted extensive research from various academic disciplines. With the inclusion of more in-depth analysis and forecasting methods, traders will be able to make an informed decision when trading. Therefore, an approach incorporating the use of historical data along with computational intelligence for analysis and forecasting is proposed in this paper. Firstly, the Gaussian Mixture Model method is applied for data partitioning on historical observations. While the antecedent part of the neurofuzzy system of AnYa type is initialised by the partitioning result, the consequent part is trained using the fuzzily weighted RLS algorithm based on the same data. Numerical examples based on the real currency exchange data demonstrated that the proposed approach trained with historical data produce promising results when used to forecast the future foreign exchange rates over a long-term period. Although implemented in an offline environment, it could potentially be utilised in real-time application in the future. (C) 2018 The Authors. Published by Elsevier Ltd.