Computational intelligence approaches and linear models in case studies of forecasting exchange rates |
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| Affiliation: | 1. Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, PO Box 1066 Blindern, 0316 Oslo, Norway;2. Department of Biology and Hjort Centre for Marine Ecosystem Dynamics, University of Bergen, PO Box 7803, 5020 Bergen, Norway;3. Knipovich Polar Research Institute of Marine Fisheries and Oceanography (PINRO), 6 Academician Knipovich Street, 183038 Murmansk, Russia;4. Uni Research and Hjort Centre for Marine Ecosystem Dynamics, PO Box 7810, 5020 Bergen, Norway;1. Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-147, Kaunas LT-51368, Lithuania;2. Hohai University, Nanjing, China |
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| Abstract: | Artificial neural networks and fuzzy systems, have gradually established themselves as a popular tool in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi–Sugeno (TS) fuzzy system are able to provide a more accurate out-of-sample forecast than the traditional auto regressive moving average (ARMA) and ARMA generalized auto regressive conditional heteroskedasticity (ARMA-GARCH) linear models. Using series of Brazilian exchange rate (R$/US$) returns with 15 min, 60 min and 120 min, daily and weekly basis, the one-step-ahead forecast performance is compared. Results indicate that forecast performance is strongly related to the series’ frequency and the forecasting evaluation shows that nonlinear models perform better than their linear counterparts. In the trade strategy based on forecasts, nonlinear models achieve higher returns when compared to a buy-and-hold strategy and to the linear models. |
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