A New Approach to Multi-variable Fuzzy Forecasting Using Picture Fuzzy Clustering and Picture Fuzzy Rule Interpolation Method

  • Pham Huy ThongEmail author
  • Le Hoang Son
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 326)


In this paper, a new approach to multi-variable fuzzy forecasting using picture fuzzy clustering and picture fuzzy rule interpolation techniques is proposed. Firstly, we partition dataset into clusters using picture fuzzy clustering algorithm. Secondly, we construct picture fuzzy rules based on given clusters. Finally, we determine the predicted outputs based on the picture fuzzy rule interpolation scheme. Our proposed approach is applied to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) data. The experimental results indicate that our method predicts better forecasting results than some relevant ones.


Fuzzy forecasting Picture Fuzzy rule interpolation method Multivariable fuzzy forecasting Picture fuzzy clustering Stock prediction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albeanu, G., Popentiu-Vladicescu, F.L.: Intuitionistic fuzzy methods in software reliability modeling. Journal of Sustainable Energy 1(1) (2010)Google Scholar
  2. 2.
    Armano, G., Marchesi, M., Murru, A.: A hybrid genetic-neural architecture for stock indexes forecasting. Information Sciences 170(1), 3–33 (2005)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets and Systems 20, 87–96 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers (1981)Google Scholar
  5. 5.
    Burillo, P., Bustince, H.: Entropy on intuitionistic fuzzy set and on interval-valued fuzzy set. Fuzzy Sets and Systems 78, 305–316 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Cardoso, G., Gomide, F.: Newspaper demand prediction and replacement model based on fuzzy clustering and rules. Information Sciences 177(21), 4799–4809 (2007)CrossRefGoogle Scholar
  7. 7.
    Celikyilmaz, A., Turksen, I.B.: Enhanced fuzzy system models with im-proved fuzzy clustering algorithm. IEEE Transactions on Fuzzy Systems 16(3), 779–794 (2008)CrossRefGoogle Scholar
  8. 8.
    Chaira, T.: A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images. Applied Soft Computing 11(2), 1711–1717 (2011)CrossRefGoogle Scholar
  9. 9.
    Chaira, T., Panwar, A.: An Atanassov’s intuitionistic Fuzzy Kernel Clustering for Medical Image segmentation. International Journal of Computational Intelligence Systems, 1–11 (2013)Google Scholar
  10. 10.
    Chang, Y.C., Chen, S.M.: Temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 3444–3449. IEEE (2009)Google Scholar
  11. 11.
    Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems 81(3), 311–319 (1996)CrossRefGoogle Scholar
  12. 12.
    Chen, S.M., Chang, Y.C.: Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. Information Sciences 180(24), 4772–4783 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Chen, S.M., Chen, C.D.: TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Transactions on Fuzzy Systems 19(1), 1–12 (2011)CrossRefGoogle Scholar
  14. 14.
    Chen, S.M., Chu, H.P., Sheu, T.W.: TAIEX forecasting using fuzzy time series and automatically generated weights of multiple factors. IEEE Transactions on Systems, Man and Cybernetics, Part A, Systems and Humans 42(6), 1485–1495 (2012)CrossRefGoogle Scholar
  15. 15.
    Chen, S.-M., Kao, P.-Y.: Forecasting the TAIEX based on fuzzy time series, PSO techniques and support vector machines. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part I. LNCS (LNAI), vol. 7802, pp. 89–98. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. 16.
    Chen, S.M., Manalu, G.M.T., Pan, J.S., Liu, H.C.: Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Transactions on Cybernetics 43(3), 1102–1117 (2013)CrossRefGoogle Scholar
  17. 17.
    Cheng, C.H., Cheng, G.W., Wang, J.W.: Multi-attribute fuzzy time series method based on fuzzy clustering. Expert Systems with Applications 34(2), 1235–1242 (2008)CrossRefGoogle Scholar
  18. 18.
    Cuong, B.C., Kreinovich, V.: Picture Fuzzy Sets - a new concept for computational intelligence problems. In: Proceeding of 2013 Third World Congress on Information and Communication Technologies (WICT), pp. 1–6 (2013)Google Scholar
  19. 19.
    Egrioglu, E., Aladag, C.H., Yolcu, U., Uslu, V.R., Erilli, N.A.: Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering. Expert Systems with Applications 38(8), 10355–10357 (2011)CrossRefGoogle Scholar
  20. 20.
    Ejegwa, P.A., Akubo, A.J., Joshua, O.M.: Intuitionistic fuzzy set and its application in career determination via normalized Euclidean distance method. European Scientific Journal 10(15) (2014)Google Scholar
  21. 21.
  22. 22.
    Huarng, K., Yu, T.H.K.: The application of neural networks to forecast fuzzy time series. Physica A: Statistical Mechanics and its Applications 363(2), 481–491 (2006)CrossRefGoogle Scholar
  23. 23.
    Huarng, K.H., Yu, T.H.K., Hsu, Y.W.: A multivariate heuristic model for fuzzy time-series forecasting. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 37(4), 836–846 (2007)CrossRefGoogle Scholar
  24. 24.
    Khashei, M., Bijari, M.: A novel hybridization of artificial neural net-works and ARIMA models for time series forecasting. Applied Soft Computing 11(2), 2664–2675 (2011)CrossRefGoogle Scholar
  25. 25.
    Pedrycz, W.: Why triangular membership functions? Fuzzy Sets and Systems 64(1), 21–30 (1994)MathSciNetCrossRefGoogle Scholar
  26. 26.
  27. 27.
    Wei, L.Y., Cheng, C.H., Wu, H.H.: A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock. Applied Soft Computing 19, 86–92 (2014)CrossRefGoogle Scholar
  28. 28.
    Yu, T.H.K., Huarng, K.H.: A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems with Applications 34(4), 2945–2952 (2008)CrossRefGoogle Scholar
  29. 29.
    Yu, T.H.K., Huarng, K.H.: Corrigendum to “A bivariate fuzzy time series model to forecast the TAIEX”. Expert Systems with Applications 34(4), 2945–2952 (2010); Expert Systems with Applications 37(7), 5529 (2010) Google Scholar
  30. 30.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zadeh, L.A.: Toward a generalized theory of uncertainty (GTU) – an outline. Information Sciences 172(1-2), 1–40 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Zadeh, L.A.: Is there a need for fuzzy logic? Information Sciences 178(13), 2751–2779 (2008)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.VNU University of Science, Vietnam National UniversityHanoiVietnam

Personalised recommendations