Financial Time Series Prediction using a Hybrid Functional Link Fuzzy Neural Network Trained by Adaptive Unscented Kalman Filter

The model proposed in this paper, is a hybridization of fuzzy neural network (FNN) and a functional link neural system for currency exchange rate prediction. The TSK-type feedforward fuzzy neural network does not take the full advantage of the use of the fuzzy rule base in accurate input-output mapping and hence a hybrid model is developed using the functional link neural network (FLANN) to construct the consequent part of the fuzzy rules. The FLANN model is used to provide an expanded nonlinear transformation to the input space thereby increasing its dimension which will be adequate to capture the nonlinearities and chaotic variations in the currency exchange time series. Further hybridizing it with Fuzzy neural network will result in a significant accuracy in day ahead currency exchange rate prediction. Currency exchange rates between US Dollar (USD) and other four currencies such as Australian Dollar (AUD), Indian Rupee (INR), Japanese Yen (JPY), and Canadian Dollar (CAD) datasets are used to validate the efficacy of the proposed FLFNN.