Non-stationary exchange rate prediction using soft computing techniques

Soft computing is widely used as it enables forecasting with fast learning capacity and adaptability, and can process data despite uncertainties and complex nonlinear relationships. Soft computing can model nonlinear relationships with better accuracy than traditional statistical and econometric models, and does not make much assumptions regarding the data set. In addition, soft computing can be used on nonlinear and nonstationary time series data when the use of conventional methods is not possible. In this paper, we compare estimates of the nonstationary USD/IDR exchange rates obtained by three soft computing methods: fuzzy time series (FTS), the artificial neural network (ANN), and the adaptive-network-based fuzzy inference system (ANFIS). The performances of these methods are compared by examining the forecast errors of the estimates against the real values. Compared to ANN and FTS, ANFIS produced better results by making predictions with the smallest root mean square error

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

A Dynamic Regularized Radial Basis Function Network for Nonlinear, Nonstationary Time Series Prediction

In this paper, constructive approximation theorems are given which show that, under certain conditions, the standard Nadaraya-Watson regression estimate (NWRE) can be considered a specially regularized form of radial basis function networks (RBFNs). From this and another related result, we deduce that regularized RBFNs are m.s. consistent, like the NWRE, for the one-step-ahead (1-SA) prediction of Markovian nonstationary, nonlinear autoregressive time series generated by i.i.d. noise processes. Additionally, choosing the regularization parameter to be asymptotically optimal gives regularized RBFNs the advantage of asymptotically realizing minimum m.s. prediction error. Two update algorithms, one with augmented networks/infinite memory and the other with fixed-size networks/finite memory, are then proposed to deal with nonstationarity induced by time-varying regression functions. For the latter algorithm, tests on several phonetically-balanced male and female speech samples show an aver..