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Exchange Rate Forecasting Through Distributed Time-Lagged Feedforward Neural Networks

In: Supply Chain And Finance

Author

Listed:
  • 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)

Abstract

Throughout 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.

Suggested Citation

  • N. G. Pavlidis & D. K. Tasoulis & G. S. Androulakis & M. N. Vrahatis, 2004. "Exchange Rate Forecasting Through Distributed Time-Lagged Feedforward Neural Networks," World Scientific Book Chapters, in: Panos M Pardalos & Athanasios Migdalas & George Baourakis (ed.), Supply Chain And Finance, chapter 17, pages 283-298, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812562586_0017
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