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A Comparative Study Among ANFIS, ANNs, and SONFIS for Volatile Time Series

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Fuzzy Information Processing (NAFIPS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 831))

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Abstract

This paper presents a comparison among ANFIS, ANNs, and a Self Organized Neuro Fuzzy Inference System (SONFIS) for time series prediction. The Turkish stock index (ISE) series is analyzed using the three methods, a statistical analysis of the residuals per method is performed, and the advantages/disadvantages per method are discussed.

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References

  1. Akaike, H.: An information criterion (AIC). Math. Sci. 14(153), 5–9 (1976)

    Google Scholar 

  2. Avellaneda, A., Ochoa, C., Figueroa-García, J.C.: A self-organizing neural fuzzy system to forecast the price of Ecopetrol shares. In: IEEE (ed.) Proceedings of 2012 IEEE Computational Intelligence for Financial Engineering and Economics Conference, pp. 1–6. IEEE (2012)

    Google Scholar 

  3. Ben, D., Dayan, R., Baselli, G., Invar, G.F.: An adaptive neuro-fuzzy method (ANFIS) for estimating single-trial movement-related potentials. Biol. Cybern. 91(2), 63–75 (2004)

    MATH  Google Scholar 

  4. Engel, R.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 1(50), 987–1007 (1982)

    Article  MathSciNet  Google Scholar 

  5. Kalenatic, D., Figueroa-García, J.C., Lopez, C.A.: A neuro-evolutive interval type-2 TSK fuzzy system for volatile weather forecasting. In: Huang, D.-S., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2010. LNCS, vol. 6215, pp. 142–149. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14922-1_19

    Chapter  Google Scholar 

  6. Figueroa-García, J.C., Soriano, J.J.: A comparison of ANFIS, ANN and DBR systems on volatile time series identification. In: IEEE (ed.) 2007 Annual Meeting of the North American Fuzzy Information Processing Society, vol. 26, pp. 321–326. IEEE (2007). https://doi.org/10.1109/NAFIPS.2007.383858

  7. Figueroa-García, J.C.: An evolutive interval type-2 TSK fuzzy logic system for volatile time series identification. In: Conference on Systems, Man and Cybernetics, pp. 1–6. IEEE (2009). https://doi.org/10.1109/ICSMC.2009.5346687

  8. Figueroa-García, J.C., Ochoa-Rey, C., Avellaneda-González, J.: A self-organized fuzzy neural network approach for rule generation of fuzzy logic systems. In: Huang, D.-S., Gupta, P., Wang, L., Gromiha, M. (eds.) ICIC 2013. CCIS, vol. 375, pp. 25–30. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39678-6_5

    Chapter  Google Scholar 

  9. Figueroa-García, J.C., Ochoa, C., Avellaneda, A.: Rule generation of fuzzy logic systems using a self-organized fuzzy neural network. Neurocomputing 151(3), 955–962 (2015). https://doi.org/10.1016/j.neucom.2014.09.079

    Article  Google Scholar 

  10. Gujarati, D.: Econometrics. McGraw Hill International, New York (2009)

    Google Scholar 

  11. Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recogn. Artif. Intell. 13(7), 1083–1101 (1999)

    Article  Google Scholar 

  12. Huang, D.S., Du, J.X.: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Netw. 19(12), 2099–2115 (2008)

    Article  Google Scholar 

  13. Jang, J.S.R.: ANFIS: adaptive neural based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  14. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Englewood Cliffs (1997)

    Google Scholar 

  15. Juang, C., Lin, C.: An on-line self-constructing neural fuzzy inference network and its application. IEEE Trans. Fuzzy Syst. 6(1), 12–32 (1998)

    Article  Google Scholar 

  16. Juang, C.F., Tsao, Y.W.: A type-2 self-organizing neural fuzzy system and its FPGA implementation. IEEE Trans. Syst. Man Cybern. Part B Cybern. 38, 1537–1548 (2008)

    Article  Google Scholar 

  17. Jang, J.-S.R.: Input selection for ANFIS learning. In: IEEE (ed.) The 15th IEEE World Conference on Computational Intelligence, Reno, p. 825. IEEE (2005)

    Google Scholar 

  18. Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty and Information. Prentice Hall, Englewood Cliffs (1992)

    MATH  Google Scholar 

  19. Ljung, G.M., Box, G.E.P.: Information theory and the extension of the maximum likelihood principle. Biometrika 65(1), 553–564 (1978)

    MathSciNet  Google Scholar 

  20. Melin, P., Soto, J., Castillo, O., Soria, J.: A new approach for time series prediction using ensembles of ANFIS models. Expert Syst. Appl. 39(3), 3494–3506 (2012). https://doi.org/10.1016/j.eswa.2011.09.040

    Article  Google Scholar 

  21. Mendel, J.M.: Computing derivatives in interval type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 12(1), 84–98 (2004)

    Article  Google Scholar 

  22. Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall, Englewood Cliffs (2001)

    MATH  Google Scholar 

  23. Numberger, A., Kruse, R.A.: A neuro-fuzzy approach to optimize hierarchical recurrent fuzzy systems. Fuzzy Optim. Decis. Making 1(2), 221 (2002)

    Article  MathSciNet  Google Scholar 

  24. Obayashi, M., Kuremoto, T., Kobayashi, K.: A self-organized fuzzy-neuro reinforcement learning system for continuous state space for autonomous robots. In: IEEE (ed.) Proceedings of CIMCA/IAWTIC/ISE, pp. 551–556. IEEE (2008)

    Google Scholar 

  25. Schwarz, G.E.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

    Article  MathSciNet  Google Scholar 

  26. Soto, J., Melin, P., Castillo, O.: Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators. Int. J. Hybrid Intell. Syst. 11(3), 211–226 (2014). https://doi.org/10.3233/HIS-140196

    Article  Google Scholar 

  27. Soto, J., Melin, P., Castillo, O.: Particle swarm optimization of the fuzzy integrators for time series prediction using ensemble of IT2FNN architectures. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems. SCI, vol. 667, pp. 141–158. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47054-2_9

    Chapter  Google Scholar 

  28. Thode, H.C.J.: Testing for Normality. Marcel Drecker Inc., New York (2002)

    Book  Google Scholar 

  29. Tukey, J.W.: The Problem of Multiple Comparisons: Course Notes. Princeton University, Princeton (1985)

    Google Scholar 

  30. Wang, L., Fsas, J.: Extracting fuzzy rules. Fuzzy Sets Fuzzy Syst. 101, 353–362 (1999)

    Article  Google Scholar 

  31. Wang, L.X., Mendel, J.M.: Back-propagation fuzzy system as nonlinear dynamic system identifiers. In: Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 8, pp. 1537–1548. IEEE (1992)

    Google Scholar 

  32. Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. In: IEEE (ed.) Proceedings of IEEE International Symposium on Control, pp. 263–268. IEEE (1991)

    Google Scholar 

  33. Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)

    Article  MathSciNet  Google Scholar 

  34. Wu, I.F., Goo, Y.J.: A neuro-fuzzy computing technique for modeling the time series of short-term \(nt/\)us exchange rates. J. Am. Acad. Bus. 7(2), 176 (2005)

    Google Scholar 

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Authors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Jairo Andres Perdomo-Tovar, Eiber Arley Galindo-Arevalo & Juan Carlos Figueroa-García

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  1. Jairo Andres Perdomo-Tovar
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  2. Eiber Arley Galindo-Arevalo
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  3. Juan Carlos Figueroa-García
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Correspondence to Juan Carlos Figueroa-García .

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Editors and Affiliations

  1. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Guilherme A. Barreto

  2. Department of Statistics & Applied Mathematics, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Ricardo Coelho

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Perdomo-Tovar, J.A., Galindo-Arevalo, E.A., Figueroa-García, J.C. (2018). A Comparative Study Among ANFIS, ANNs, and SONFIS for Volatile Time Series. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_22

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  • DOI: https://doi.org/10.1007/978-3-319-95312-0_22

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-95312-0

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