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Stock Trend Prediction Using Neurofuzzy Predictors Based on Brain Emotional Learning Algorithm

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Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

Short term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. To predict stock trends, we exploit Emotional Learning Based Fuzzy Inference System (ELFIS). ELFIS has the advantage of low computational complexity in comparison with other multi-objective optimization methods. The performance of ELFIS in the prediction of stock prices will be compared with that of Adaptive Network Based Fuzzy Inference System (ANFIS). Simulations show better performance for ELFIS.

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References

  1. Balkenius, C., Moren, J.: A Computational Model Of Emotional Conditionin In The Brain. In: Proc. Grounding Emotions in Adaptive Systems, Zurich (1998)

    Google Scholar 

  2. Moren, J., Balkenius, C.: A Computational Model of Emotional Learning in The Amygdala. In: Proc. 6th Int. conf. Sim. Adaptive Behavior, MIT Press, Cambridge (2000)

    Google Scholar 

  3. Lucas, C., Shahmirzadi, D., Sheikholeslami, N.: Introducing BELBIC: Brain Emotional Learning Based Intelligent Controller. To appear in International Journal of Intelligent Automation and Soft Computing, Autosoft (2004)

    Google Scholar 

  4. Lucas, C., Abbaspour, A., Gholipour, A., Araabi, B.N., Fatourechi, M.: Enhancing the Performance of Neurofuzzy Predictors by Emotional Learning Algorithm. Informatica (October 2003)

    Google Scholar 

  5. Tan, H., Prokhorov, D., Wunsch, D.: Probabilistic and time-delay neural-network techniques for conservative short-term stock trend prediction. In: Proc. World Congr. N.N., Washington D.C (July 1995)

    Google Scholar 

  6. Kreesuradej, W., Wunsch, D., Lane, M.: Time-delay neural network for small time series data sets. In: Proc. World Congr. N.N., CA (1994)

    Google Scholar 

  7. Saad, E., Prokhorov, D., Wunsch, D.: Advanced neural-network training methods for low false alarm stock trend prediction. In: Proc. IEEE Int. Conf. N.N., Washington D.C., pp. 2021–2026 (1996)

    Google Scholar 

  8. Kavli, T.: ASMOD: An algorithm for adaptive spline modeling of observation data. Int. Journal of Control 58(4), 947–967 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  9. Jang, J.R.: ANFIS: Adaptive network based fuzzy inference system. IEEE Tran. On Systems, Man and Cybernetics 23(3), 665–685 (1993)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Young Researchers Club, Islamic Azad University, Tehran, Iran

    Mahdi Jalili-Kharaajoo

Authors
  1. Mahdi Jalili-Kharaajoo
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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Jalili-Kharaajoo, M. (2004). Stock Trend Prediction Using Neurofuzzy Predictors Based on Brain Emotional Learning Algorithm. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_43

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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