Evolving Recurrent Neural Network using Cartesian Genetic Programming to Predict The Trend in Foreign Currency Exchange Rates
Created by W.Langdon from
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- @Article{journals/aai/ZafariKRM14,
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author = "Faheem Zafari and Gul Muhammad Khan and
Mehreen Rehman and Sahibzada Ali Mahmud",
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title = "Evolving Recurrent Neural Network using Cartesian
Genetic Programming to Predict The Trend in Foreign
Currency Exchange Rates",
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journal = "Applied Artificial Intelligence",
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year = "2014",
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number = "6",
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volume = "28",
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pages = "597--628",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
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bibdate = "2014-07-28",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/aai/aai28.html#ZafariKRM14",
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ISSN = "0883-9514",
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broken = "doi:10.1080/08839514.2014.923174",
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URL = "https://www.tandfonline.com/doi/pdf/10.1080/08839514.2014.923174",
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broken = "http://www.tandfonline.com/doi/abs/10.1080/08839514.2014.923174",
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size = "32 pages",
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abstract = "Forecasting the foreign exchange rate is an uphill
task. Numerous methods have been used over the years to
develop an efficient and reliable network for
forecasting the foreign exchange rate. This study uses
recurrent neural networks (RNNs) for forecasting the
foreign currency exchange rates. Cartesian genetic
programming (CGP) is used for evolving the artificial
neural network (ANN) to produce the prediction model.
RNNs that are evolved through CGP have shown great
promise in time series forecasting. The proposed
approach uses the trends present in the historical data
for its training purpose. Thirteen different currencies
along with the trade-weighted index (TWI) and special
drawing rights (SDR) is used for the performance
analysis of recurrent Cartesian genetic
programming-based artificial neural networks (RCGPANN)
in comparison with various other prediction models
proposed to date. The experimental results show that
RCGPANN is not only capable of obtaining an accurate
but also a computationally efficient prediction model
for the foreign currency exchange rates. The results
demonstrated a prediction accuracy of 98.872 percent
(using 6 neurons only) for a single-day prediction in
advance and, on average, 92percent for predicting a
1000 days' exchange rate in advance based on ten days
of data history. The results prove RCGPANN to be the
ultimate choice for any time series data prediction,
and its capabilities can be explored in a range of
other fields.",
- }
Genetic Programming entries for
Faheem Zafari
Gul Muhammad Khan
Mehreen Rehman
Sahibzada Ali Mahmud
Citations