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Fuzzy Rule-Based Ensemble Forecasting: Introductory Study

  • Conference paper
Synergies of Soft Computing and Statistics for Intelligent Data Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 190))

Abstract

There is no individual forecasting method that is generally for any given time series better than any other method. Thus, no matter the efficiency of a chosen method, there always exists a danger that for a given time series the chosen method is inappropriate. To overcome such a problem and avoid the above mentioned danger, distinct ensemble techniques that combine more individual forecasting methods are designed. These techniques basically construct a forecast as a linear combination of forecasts by individual methods. In this contribution, we construct a novel ensemble technique that determines the weights based on time series features. The protocol that carries a knowledge how to combine the individual forecasts is a fuzzy rule base (linguistic description). An exhaustive experimental justification is provided. The suggested ensemble approach based on fuzzy rules demonstrates both, lower forecasting error and higher robustness.

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References

  1. Adya, M., Armstrong, J.S., Collopy, F., Kennedy, M.: An application of rule-based forecasting to a situation lacking domain knowledge. Int. J. Forecasting 16, 477–484 (2000)

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of 20th Int. Conf. on Very Large Databases, pp. 487–499. AAAI Press (1994)

    Google Scholar 

  3. Armstrong, J.S.: Evaluating methods. In: Principles of Forecasting: A Handbook for Reasearchers and Practitioners. Kluwer, Dordrecht (2001)

    Google Scholar 

  4. Armstrong, J.S., Collopy, F.: Error measures for generalizing about forecasting methods: Empirical comparisons. Int. J. Forecasting 8, 69–80 (1992)

    Article  Google Scholar 

  5. Armstrong, J.S., Adya, M., Collopy, F.: Rule-Based Forecasting Using Judgment in Time Series Extrapolation. In: Principles of Forecasting: A Handbook for Reasearchers and Practitioners. Kluwer, Dordrecht (2001)

    Google Scholar 

  6. Barrow, D.K., Crone, S.F., Kourentzes, N.: An Evaluation of Neural Network Ensembles and Model Selection for Time Series Prediction. In: Proc. of 2010 IEEE IJCNN. IEEE Press, Barcelona (2010)

    Google Scholar 

  7. Bates, J.M., Granger, C.W.J.: Combination of Forecasts. Oper. Res. Q 20, 451–468 (1969)

    Article  Google Scholar 

  8. Bělohlávek, R., Novák, V.: Learning Rule Base of the Linguistic Expert Systems. Soft Comput. 7, 79–88 (2002)

    Article  MATH  Google Scholar 

  9. Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976)

    MATH  Google Scholar 

  10. Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., Winkler, R.: The Accuracy of Extrapolation (Time-Series) Methods — Results of a Forecasting Competition. J. Forecasting 1, 111–153 (1982)

    Article  Google Scholar 

  11. Collopy, F., Armstrong, J.S.: Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations. Manage. Sci. 38, 1394–1414 (1992)

    Article  Google Scholar 

  12. Cortez, P., Rocha, M., Neves, J.: Evolving Time Series Forecasting ARMA Models. J. Heuristics 10, 415–429 (2004)

    Article  Google Scholar 

  13. Crone, S.F., Hibon, M., Nikolopoulos, K.: Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. Int. J. Forecasting 27, 635–660 (2011)

    Article  Google Scholar 

  14. Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74, 427–431 (1979)

    MathSciNet  MATH  Google Scholar 

  15. Dvořák, A., Štěpnička, M., Vavříčková, L.: Redundancies in systems of fuzzy/linguistic IF-THEN rules. In: Proc. EUSFLAT 2011, pp. 1022–1029 (2011)

    Google Scholar 

  16. Hamilton, J.D.: Time Series Analysis. Princeton University Press (1994)

    Google Scholar 

  17. Hájek, P.: The question of a general concept of the GUHA method. Kybernetika 4, 505–515 (1968)

    MATH  Google Scholar 

  18. Hájek, P., Havránek, T.: Mechanizing hypothesis formation: Mathematical foundations for a general theory. Springer, Berlin (1978)

    Book  MATH  Google Scholar 

  19. Hyndman, R., Koehler, A.: Another look at measures of forecast accuracy. Int. J. Forecasting 22, 679–688 (2006)

    Article  Google Scholar 

  20. Kupka, J., Tomanová, I.: Some extensions of mining of linguistic associations. Neural Netw. World 20, 27–44 (2010)

    Google Scholar 

  21. Lemke, C., Gabrys, B.: Meta-learning for time series forecasting in the NN GC1 competition. In: Proc. of 2010 FUZZ-IEEE, Barcelona, Spain. IEEE Press (2010)

    Google Scholar 

  22. MacKinnon, J.G.: Numerical Distribution Functions for Unit Root and Cointegration Tests. J. Appl. Econom. 11, 601–618 (1996)

    Article  Google Scholar 

  23. Makridakis, S., Hibon, M.: The M3-Competition: Results, Conclusions and Implications. Int. J. Forecasting 16, 451–476 (2000)

    Article  Google Scholar 

  24. Makridakis, S., Wheelwright, S., Hyndman, R.: Forecasting methods and applications, 3rd edn. John Wiley & Sons (2008)

    Google Scholar 

  25. Newbold, P., Granger, C.W.J.: Experience with forecasting univariate time series and combination of forecasts. J. Roy. Stat. Soc. A Sta. 137, 131–165 (1974)

    Article  MathSciNet  Google Scholar 

  26. Novák, V.: A comprehensive theory of trichotomous evaluative linguistic expressions. Fuzzy Set. Syst. 159, 2939–2969 (2008)

    Article  MATH  Google Scholar 

  27. Novák, V.: Perception-based logical deduction. In: Computational Intelligence, Theory and Applications. ASC, pp. 237–250. Springer, Berlin (2005)

    Chapter  Google Scholar 

  28. Novák, V., Perfilieva, I., Dvořák, A., Chen, Q., Wei, Q., Yan, P.: Mining pure linguistic associations from numerical data. Int. J. Approx. Reason. 48, 4–22 (2008)

    Article  MATH  Google Scholar 

  29. Štěpnička, M., Donate, J.P., Cortez, P., Vavříčková, L., Gutierrez, G.: Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods. In: Proc. of EUSFLAT 2011, pp. 464–471 (2011)

    Google Scholar 

  30. Štěpnička, M., Dvořák, A., Pavliska, V., Vavříčková, L.: A linguistic approach to time series modeling with the help of the F-transform. Fuzzy Set. Syst. 180, 164–184 (2011)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Informatics and Computers, Faculty of Science, University of Ostrava, Ostrava, Czech Republic

    David Sikora

  2. Centre of Excellence, IT4Innovations, Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Ostrava, Czech Republic

    Martin Štěpnička & Lenka Vavříčková

Authors
  1. David Sikora
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  2. Martin Štěpnička
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  3. Lenka Vavříčková
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Corresponding author

Correspondence to David Sikora .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke University of Magdebur, Geb. 29, Raum 008, Universitätsplatz 2, Magdeburg, 39106, Germany

    Rudolf Kruse

  2. , FB Informatik & Informationswissenschaft, University of Konstanz, Konstanz, 78457, Germany

    Michael R. Berthold

  3. of Magdeburg, Faculty of Computer Science, Otto-von-Guericke University, Geb. 29, Universitätsplatz 2 008, Magdeburg, 39106, Germany

    Christian Moewes

  4. , Department of Statistics and OR, University of Oviedo, C/ Calvo Sotelo, s/n, Oviedo, 33007, Spain

    María Ángeles Gil

  5. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw, 01-447, Poland

    Przemysław Grzegorzewski

  6. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw, 01-447, Poland

    Olgierd Hryniewicz

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Sikora, D., Štěpnička, M., Vavříčková, L. (2013). Fuzzy Rule-Based Ensemble Forecasting: Introductory Study. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_41

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  • DOI: https://doi.org/10.1007/978-3-642-33042-1_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33041-4

  • Online ISBN: 978-3-642-33042-1

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