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Exchange rate forecasting through distributed time-lagged feedforward neural networks

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Exchange rate forecasting through distributed time-lagged feedforward neural networks. / Pavlidis, N. G.; Tasoulis, D. K.; Androulakis, G. S. et al.
Supply Chain And Finance. World Scientific Publishing Co., 2004. p. 283-298.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Pavlidis, NG, Tasoulis, DK, Androulakis, GS & Vrahatis, MN 2004, Exchange rate forecasting through distributed time-lagged feedforward neural networks. in Supply Chain And Finance. World Scientific Publishing Co., pp. 283-298. https://doi.org/10.1142/9789812562586_0017

APA

Pavlidis, N. G., Tasoulis, D. K., Androulakis, G. S., & Vrahatis, M. N. (2004). Exchange rate forecasting through distributed time-lagged feedforward neural networks. In Supply Chain And Finance (pp. 283-298). World Scientific Publishing Co.. https://doi.org/10.1142/9789812562586_0017

Vancouver

Pavlidis NG, Tasoulis DK, Androulakis GS, Vrahatis MN. Exchange rate forecasting through distributed time-lagged feedforward neural networks. In Supply Chain And Finance. World Scientific Publishing Co. 2004. p. 283-298 doi: 10.1142/9789812562586_0017

Author

Pavlidis, N. G. ; Tasoulis, D. K. ; Androulakis, G. S. et al. / Exchange rate forecasting through distributed time-lagged feedforward neural networks. Supply Chain And Finance. World Scientific Publishing Co., 2004. pp. 283-298

Bibtex

@inbook{61c5f7f8ba8146949e38f4a7b5f564bb,
title = "Exchange rate forecasting through distributed time-lagged feedforward neural networks",
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 alternat ive algorithms.",
keywords = "Artificial neural networks, Differential evolution algorithms, Time series prediction",
author = "Pavlidis, {N. G.} and Tasoulis, {D. K.} and Androulakis, {G. S.} and Vrahatis, {Michael N.}",
note = "Publisher Copyright: {\textcopyright} 2004 by World Scientific Publishing Co. Re. Ltd.",
year = "2004",
month = jan,
day = "1",
doi = "10.1142/9789812562586_0017",
language = "English",
pages = "283--298",
booktitle = "Supply Chain And Finance",
publisher = "World Scientific Publishing Co.",
address = "United States",

}

RIS

TY - CHAP

T1 - Exchange rate forecasting through distributed time-lagged feedforward neural networks

AU - Pavlidis, N. G.

AU - Tasoulis, D. K.

AU - Androulakis, G. S.

AU - Vrahatis, Michael N.

N1 - Publisher Copyright: © 2004 by World Scientific Publishing Co. Re. Ltd.

PY - 2004/1/1

Y1 - 2004/1/1

N2 - 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 alternat ive algorithms.

AB - 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 alternat ive algorithms.

KW - Artificial neural networks

KW - Differential evolution algorithms

KW - Time series prediction

UR - http://www.scopus.com/inward/record.url?scp=85115970973&partnerID=8YFLogxK

U2 - 10.1142/9789812562586_0017

DO - 10.1142/9789812562586_0017

M3 - Chapter

AN - SCOPUS:85115970973

SP - 283

EP - 298

BT - Supply Chain And Finance

PB - World Scientific Publishing Co.

ER -