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Training Set Fuzzification Towards Prediction Improvement

  • Eva VolnaEmail author
  • Jaroslav Zacek
  • Robert Jarusek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)

Abstract

This article presents a method of fuzzification of variables using a histogram. This approach is used when creating an output vector of a training set that forms linguistic variables. An appropriate transformation of an input vector of the training sets was also proposed. Both of the aforesaid procedures were described in detail in the article. An extensive comparative experimental study with the following outcomes was carried out. The neural net which was adapted by the transformed training set showed a significantly better prediction than a neural network which was adapted by a training set without making any changes. The results of this experimental study were analyzed in the conclusion.

Keywords

Continuous distribution Histogram Linguistic variable Neural network Prediction 

Notes

Acknowledgments

The research described here has been financially supported by University of Ostrava grant SGS/PRF/2017.

References

  1. 1.
    Dvořák, A., Habiballa, H., Novák, V., Pavliska, V.: The concept of LFLC 2000 - its specificity, realization and power of applications. Comput. Ind. 51(3), 269–280 (2003)CrossRefGoogle Scholar
  2. 2.
    Fausett, L.V.: Fundamentals of Neural Networks. Prentice-Hall Inc., Englewood Cliffs (1994)zbMATHGoogle Scholar
  3. 3.
    Rather, A.M., Agarwal, A., Sastry, V.N.: Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst. Appl. 42(6), 3234–3241 (2015)CrossRefGoogle Scholar
  4. 4.
    Janosek, M., Volna, E., Kotyrba, M.: Knowledge discovery in dynamic data using neural networks. Cluster Comput. 18(4), 1411–1421 (2015)CrossRefGoogle Scholar
  5. 5.
    Masters, T.: Practical Neural Networks Recipes in C++. Academic Press, San Diego (1993)zbMATHGoogle Scholar
  6. 6.
    Pal, S.K., Pabitra, M.: Case generation using rough sets with fuzzy representation. IEEE Trans. Knowl. Data Eng. 16(3), 293–300 (2004)CrossRefGoogle Scholar
  7. 7.
    Rumsey, D.J.: Probability for dummies. Wiley, Hoboken (2006)Google Scholar
  8. 8.
    Singh, P.: Neuro-fuzzy hybridized model for seasonal rainfall forecasting: a case study in stock index forecasting. In: Bhattacharyya, S., Dutta, P., Chakraborty, S. (eds.) Hybrid Soft Computing Approaches. SCI, vol. 611, pp. 361–385. Springer, New Delhi (2016). doi: 10.1007/978-81-322-2544-7_12 CrossRefGoogle Scholar
  9. 9.
    Teoh, H.J., Cheng, C.H., Chu, H.H., Chen, J.S.: Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets. Data Knowl. Eng. 67(1), 103–117 (2008)CrossRefGoogle Scholar
  10. 10.
    Yu, T.H.K., Huarng, K.H.: A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst. Appl. 34(4), 2945–2952 (2008)CrossRefGoogle Scholar
  11. 11.
    Wei, L.Y.: A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl. Soft Comput. 42, 368–376 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Informatics and ComputersUniversity of OstravaOstravaCzech Republic

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