EXCHANGE RATE FORECASTING THROUGH DISTRIBUTED TIME-LAGGED FEEDFORWARD NEURAL NETWORKS

N. G. Pavlidis, D. K. Tasoulis, G. S. Androulakis and M. N. Vrahatis
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N. G. Pavlidis: Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, GR–26110 Patras, Greece
D. K. Tasoulis: Department of Mathematics, UPAIRC, University of Patras, GR–26110 Patras, Greece
G. S. Androulakis: Computer Technology Institute (CTI), UPAIRC, University of Patras, GR–26110 Patras, Greece
M. N. Vrahatis: Department of Mathematics, UPAIRC, University of Patras, GR–26110 Patras, Greece

Chapter 17 in Supply Chain and Finance, 2004, pp 283-298 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractThroughout the last decade, the application of Artificial Neural Networks in the areas of financial and economic time series forecasting has been rapidly expanding. The present chapter investigates the ability of Distributed Time Lagged Feedforward Networks (DTLFN), trained through a popular Differential Evolution (DE) algorithm, to forecast the short–term behavior of the daily exchange rate of the Euro against the US Dollar. Performance is contrasted with that of focused time lagged feedforward networks, as well as with DTLFNs trained through alternative algorithms.

Keywords: Finance; Supply Chain; E-Commerce; Optimization; Mathematical Modeling; Operations Research (search for similar items in EconPapers)
Date: 2004
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