Evolving Recurrent Neural Network using Cartesian Genetic Programming to Predict The Trend in Foreign Currency Exchange Rates

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@Article{journals/aai/ZafariKRM14,
  author =       "Faheem Zafari and Gul Muhammad Khan and 
                 Mehreen Rehman and Sahibzada Ali Mahmud",
  title =        "Evolving Recurrent Neural Network using Cartesian
                 Genetic Programming to Predict The Trend in Foreign
                 Currency Exchange Rates",
  journal =      "Applied Artificial Intelligence",
  year =         "2014",
  number =       "6",
  volume =       "28",
  pages =        "597--628",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2014-07-28",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/aai/aai28.html#ZafariKRM14",
  ISSN =         "0883-9514",
  broken =       "doi:10.1080/08839514.2014.923174",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/08839514.2014.923174",
  size =         "32 pages",
  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