• Jesus SotoEmail author
  • Patricia Melin
  • Oscar Castillo
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Based on the evolution of a variable or a set of variables given in a time series, to predict future values of this variable we should seek the dynamic laws governing the real state of the system over time. This preliminary step is the prediction modeling process. In short, time series analysis aims at drawing conclusions about a complex system using past data.


  1. 1.
    Brocklebank, J.C., Dickey, D.A.: SAS for Forecasting Series, pp. 6–140. SAS Institute Inc., Cary, NC, USA (2003)zbMATHGoogle Scholar
  2. 2.
    Brockwell, P.D., Davis, R.A.: Introduction to Time Series and Forecasting, pp. 1–219. Springer, New York (2002)zbMATHGoogle Scholar
  3. 3.
    Horikowa, S., Furuhashi T., Uchikawa, Y.: On fuzzy modeling using fuzzy neural networks with the backpropagation algorithm. IEEE Trans. Neural Netw. 3 (1992)Google Scholar
  4. 4.
    Melin, P., Soto, J., Castillo, O., Soria, J.: A new approach for time series prediction using ensembles of ANFIS models. Experts Syst. Appl. 39(3), 3494–3506 (2012)CrossRefGoogle Scholar
  5. 5.
    Jang, J.S.R.: ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1992)Google Scholar
  6. 6.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and Soft Computing. Prentice-Hall, New York (1997)Google Scholar
  7. 7.
    Lin, Y.C., Lee, C.H.: System identification and adaptive filter using a novel fuzzy neuro system. Int. J. Comput. Cogn. 5(1), 2 (2007)Google Scholar
  8. 8.
    Hagras, H.: Comments on dynamical optimal training for interval type-2 fuzzy neural network (T2FNN). IEEE Trans. Syst. Man Cybern. Part B 36(5), 1206–1209 (2006)CrossRefGoogle Scholar
  9. 9.
    Wang, C.H., Cheng, C.S., Lee, T.T.: Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN). IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34(3), 1462–1477 (2004)CrossRefGoogle Scholar
  10. 10.
    Lee, C.H., Lin, Y.C.: Type-2 fuzzy neuro system via input-to-state-stability approach. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) International Symposium on Neural Networks, vol. 4492, pp. 317–327. Springer, Heidelberg, LNCS (2007)Google Scholar
  11. 11.
    Lee, C.H., Hong, J.L., Lin, Y.C., Lai, W.Y.: Type-2 fuzzy neural network systems and learning. Int. J. Comput. Cogn. 1(4), 79–90 (2003)Google Scholar
  12. 12.
    Ascia, G., Catania, V., Panno, D.: An integrated fuzzy-GA approach for buffer management. IEEE Trans. Fuzzy Syst. 14(4), 528–541 (2006)CrossRefGoogle Scholar
  13. 13.
    Pedrycz, W.: Fuzzy Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (1997)CrossRefzbMATHGoogle Scholar
  14. 14.
    Chiou, Y.C., Lan, L.W.: Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method. Fuzzy Sets Syst. 152(3), 617–635 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans. Fuzzy Syst. 3, 260–270 (1995)CrossRefGoogle Scholar
  16. 16.
    Gaxiola, F., Melin, P., Valdez, F., Castillo, O.: Optimization of type-2 fuzzy weight for neural network using genetic algorithm and particle swarm optimization. In: World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 22–28 (2013)Google Scholar
  17. 17.
    Wu, D., Wan-Tan, W.: Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Eng. Appl. Artif. Intell. 19(8), 829–841 (2006)CrossRefGoogle Scholar
  18. 18.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7, 1–13 (1975)CrossRefzbMATHGoogle Scholar
  19. 19.
    Zadeh, L.A.: Fuzzy logic, neural networks and soft computing. Commun. ACM 37(3), 77–84 (1994)CrossRefGoogle Scholar
  20. 20.
    Pedrycz, W.: Fuzzy Modelling: Paradigms and Practice. Kluwer Academic Press, Dordrecht (1996)CrossRefzbMATHGoogle Scholar
  21. 21.
    Takagi, T., Sugeno, M.: Derivation of fuzzy control rules from human operation control actions. In: Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, pp. 55–60 (1983)Google Scholar
  22. 22.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)Google Scholar
  23. 23.
    Karnik, N.N., Mendel, J.M.: Applications of type-2 fuzzy logic systems to forecasting of time-series. Inf. Sci. 120, 89–111 (1999)CrossRefzbMATHGoogle Scholar
  24. 24.
    Wu, D., Mendel, J.M.: A vector similarity measure for interval type-2 fuzzy sets and type-1 fuzzy sets. Inf. Sci. 178, 381–402 (2008)CrossRefzbMATHGoogle Scholar
  25. 25.
    Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, NJ (2001)zbMATHGoogle Scholar
  26. 26.
    Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. PWS Publishing, Boston, MA (1996)Google Scholar
  27. 27.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, NJ (2003)zbMATHGoogle Scholar
  28. 28.
    Haykin, S.: Adaptive Filter Theory. Prentice Hall, Englewood Cliffs. ISBN 0-13-048434-2 (2002)Google Scholar
  29. 29.
    Pulido, M., Melin, P., Castillo, O.: Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange. Inf. Sci. 280, 188–204 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    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 Intel. Syst. 11(3), 211–226 (2014)CrossRefGoogle Scholar
  31. 31.
    Bonissone, P.P., Subbu, R., Eklund, N., Kiehl, T.R.: Evolutionary algorithms + domain knowledge = real-world evolutionary computation. IEEE Trans. Evol. Comput. 10(3), 256–280 (2006)CrossRefGoogle Scholar
  32. 32.
    Engelbrech, P.: Fundamentals of Computational of Swarm Intelligence: Basic Particle Swarm Optimization, pp. 93–129. Wiley, New York (2005)Google Scholar
  33. 33.
    Deb, K.: A population-based algorithm-generator for real-parameter optimization. Springer, Heidelberg (2005)zbMATHGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico

Personalised recommendations