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Learning Trends on the Fly in Time Series Data Using Plastic CGP Evolved Recurrent Neural Networks

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Artificial Neural Networks and Machine Learning – ICANN 2018 (ICANN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11141))

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Abstract

An approach of Direct Online Learning (DOL) to incorporate developmental plasticity in Recurrent Neural Networks termed as Plastic Cartesian Genetic Programming evolved Recurrent Neural Network (PCGPRNN), is proposed to exploit the trends in the data of the foreign currency to forecast the future currency rates, while reshaping its connectivity, biasing factors and selecting various parameters from the input vector ‘on the fly’ according to the traversed trends. The developed model learns in real time and exhibits the optimum topology for the best possible output using neuro-evolution. The network performance is observed in a range of scenarios with varying network parameters and various currencies and trading indexes obtaining competitive results. Networks trained to predict single instances are further explored in independent scenarios to predict various time intervals in advance, achieving remarkable results.

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References

  1. Philip, A.A., Tofiki, A.A., Bidemi, A.A.: Artificial neural network model for forecasting foreign exchange rate. World Comput. Sci. Inf. Technol. J. 1(3), 110–118 (2011)

    Google Scholar 

  2. Khan, G.M., Nayab, D., Mehmud, S.A., Zafar, M.H.: Evolving dynamic forecasting model for foreign currency exchange rates using plastic neural networks. In: IEEE 12th International Conference on Machine Learning and Applications ICMLA (2013)

    Google Scholar 

  3. Nayab, D., Muhammad Khan, G., Mahmud, S.A.: Prediction of foreign currency exchange rates using CGPANN. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013. CCIS, vol. 383, pp. 91–101. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41013-0_10

    Chapter  Google Scholar 

  4. Kryuchin, O.V., Arzamastsev, A.A., Troitzsch, K.G.: The prediction of currency exchange rates using artificial neural networks. Exch. Organ. Behav. Teach. J., no. 4 (2011)

    Google Scholar 

  5. Risi, S., Stanley, Kenneth O.: Indirectly encoding neural plasticity as a pattern of local rules. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, J.-A., Mouret, J.-B. (eds.) SAB 2010. LNCS (LNAI), vol. 6226, pp. 533–543. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15193-4_50

    Chapter  Google Scholar 

  6. Kadilar, C., Alada, H.: Forecasting the exchange rate series with ANN: the case of Turkey. Econ. Stat. Chang. 9, 17–29 (2009)

    Google Scholar 

  7. Galeshchuk, S., Mukherjee, S.: Deep networks for predicting direction of change in foreign exchange rates. Intell. Syst. Account., Financ. Manag. 24, 100–110 (2017)

    Article  Google Scholar 

  8. Khan, M.M., Khan, G.M., Miller, J.F.: Efficient representation of recurrent neural networks for Markovian/non-Markovian non-linear control problems. In: International Conference on Intelligent Systems Design and Applications, pp. 615–620 (2010)

    Google Scholar 

  9. Khan, M.M., Khan, G.M., Miller, J.F.: Developmental plasticity in cartesian genetic programming artificial neural networks. In: Proceedings of the International Conference on Informatics in Control, Automation and Robotics, pp. 449–458 (2011)

    Google Scholar 

  10. Cangelosi, A., Nolfi, S., Parisi, D.: Cell division and migration in a ‘genotype’ for neural networks. Netw. Comput. Neural Syst. 5, 497–515 (1994)

    Article  Google Scholar 

  11. Pacelli, V., Bavelacqua, V., Azzollini, M.: An artificial neural network model to forecast exchange rates. J. Int. Learn. Syst. Appl. 3(2A), 57–69 (2011)

    Google Scholar 

  12. Upegui, A., Perez-Uribe, A., Thoma, Y., Sanchez, E.: Neural development on the Ubichip by means of dynamic routing mechanisms. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 392–401. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85857-7_35

    Chapter  Google Scholar 

  13. Miller, J.F.: Cartesian Genetic Programming. Natural Computing Series. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-17310-3

    Book  MATH  Google Scholar 

  14. Floreano, D., Urzelai, J.: Evolutionary robots with on-line self-organization and behavioral fitness. Neural Netw. 13(4), 431–443 (2000)

    Article  Google Scholar 

  15. Chen, A.P., Hsu, Y.C., Hu, K.F.: A hybrid forecasting model for foreign exchange rate based on a multi-neural network. In: Fourth International Conference on Natural Computation, ICNC, vol. 5, pp. 293–298 (2008)

    Google Scholar 

  16. Coleman, O.J., Blair, A.D.: Evolving plastic neural networks for online learning: review and future directions. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS (LNAI), vol. 7691, pp. 326–337. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35101-3_28

    Chapter  Google Scholar 

  17. Nolfi, S., Miglino, O., Parisi, D.: Phenotypic plasticity in evolving neural networks. In: Proceedings of the International Conference from Perception to Action, pp. 146–157. IEEE Press (1994)

    Google Scholar 

  18. Khan, G.M., Miller, J.F., Halliday, D.M.: A developmental model of neural computation using cartesian genetic programming. In: Proceedings of the Genetic and Evolutionary Computation (Companion), pp. 2535–2542. ACM (2007)

    Google Scholar 

  19. Massobrio, P., et al.: In vitro studies of neuronal networks and synaptic plasticity in invertebrates and in mammals using multielectrode arrays. Neural Plast. (2015)

    Google Scholar 

  20. Zenke, F., Agnes, E.J., Gerstner, W.: Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nat. Commun. 21(6), 6922 (2015)

    Article  Google Scholar 

  21. Ravi, V., Lal, R., Kiran, N.R.: Foreign exchange rate prediction using computational intelligence methods. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 4, 659–670 (2012)

    Google Scholar 

  22. FOREX Tutorial: Economic Theories, Models, Feeds & Data Available: http://www.investopedia.com/university/forexmarket/forex5.asp.Accessed:September. Accessed Sep 2017

  23. Patel, P.J., Patel, N.J., Patel, A.R.: Factors affecting currency exchange rate, economical formulas and prediction models. International Journal of Application or Innovation in Engineering Managment. 3, 53–56 (2014)

    Google Scholar 

  24. Schwaerzel, R., Bylander, T.: Predicting currency exchange rates by genetic programming with trigonometric functions and high-order statistics. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation. ACM (2006)

    Google Scholar 

  25. Alvarez Diaz, M.: Speculative strategies in the foreign exchange market based on genetic programming predictions. Appl. Financ. Econ. 20(6), 465–476 (2010)

    Article  Google Scholar 

  26. Shylajan, C.S., Sreejesh, S., Suresh, K.G.: Rupee-dollar exchange rate and macroeconomic fundamentals: an empirical analysis using flexible-price monetary model. J. Int. Bus. Econ. 12(2), 89–105 (2011)

    Google Scholar 

  27. Shioda, K., Deng, S., Sakurai, A.: Prediction of foreign exchange market states with support vector machine. In: 2011 10th International Conference on Machine Learning and Applications and Workshops (ICMLA), vol. 1. IEEE (2011)

    Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, UET Peshawar, Peshawar, Pakistan

    Gul Mummad Khan

  2. Computer System Engineering Department, UET Peshawar, Peshawar, Pakistan

    Durr-e-Nayab

Authors
  1. Gul Mummad Khan
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  2. Durr-e-Nayab
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Corresponding author

Correspondence to Gul Mummad Khan .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Khan, G.M., Durr-e-Nayab (2018). Learning Trends on the Fly in Time Series Data Using Plastic CGP Evolved Recurrent Neural Networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_20

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  • DOI: https://doi.org/10.1007/978-3-030-01424-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01423-0

  • Online ISBN: 978-3-030-01424-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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