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A New Hybrid Model Based on Triple Exponential Smoothing and Fuzzy Time Series for Forecasting Seasonal Time Series

  • A. J. Saleena
  • C. Jessy John
Conference paper
  • 51 Downloads
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 307)

Abstract

Triple exponential smoothing is one of the prominent linear models for seasonal time series forecasting. Fuzzy time series forecasting is originated as a new advent for forecasting the data which is imprecise and vague. In this work, we proposed a methodology using both triple exponential smoothing and fuzzy time series. It has the advantage of modelling aspects in linear and non-linear setup. Empirical results with real-world data sets show that the hybrid model is an efficient one based on forecasting accuracy than the component models used individually.

Keywords

Fuzzy time series Hybrid Triple exponential smoothing 

References

  1. 1.
    R.G. Brown, Statistical Forecasting for Inventory Control (Mc-Graw/Hill, 1959)Google Scholar
  2. 2.
    R.G. Brown, Smoothing, Forecasting and Prediction of Discrete Time Series (1963), p. 238Google Scholar
  3. 3.
    P.R. Winters, Forecasting sales by exponentially weighted moving averages. Manag. Sci. 6(3), 324–342 (1960)MathSciNetCrossRefGoogle Scholar
  4. 4.
    L.A. Zadeh, Information and control. Fuzzy Sets 8(3), 338–353 (1965)Google Scholar
  5. 5.
    L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. (1), 28–44 (1973)MathSciNetCrossRefGoogle Scholar
  6. 6.
    L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8(3), 199–249 (1975)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Q. Song, B.S. Chissom, Fuzzy time series and its models. Fuzzy Sets Syst. 54(3), 269–277 (1993)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Q. Song, B.S. Chissom, Forecasting enrollments with fuzzy time series. Part I. Fuzzy Sets Syst. 54(1), 1–9 (1993)CrossRefGoogle Scholar
  9. 9.
    S.-M. Chen, Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81(3), 311–319 (1996)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kunhuang Huarng, Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst. 123(3), 387–394 (2001)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Yu. Hui-Kuang, Weighted fuzzy time series models for TAIEX forecasting. Phys. A: Stat. Mech. Appl. 3–4, 609–624 (2005)Google Scholar
  12. 12.
    C.-H. Cheng, T.-L. Chen, H.J. Teoh, C.-H. Chiang, Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Syst. Appl. 34(2), 1126–1132 (2008)CrossRefGoogle Scholar
  13. 13.
    U. Yolcu, E. Egrioglu, V.R. Uslu, M.A. Basaran, C.H. Aladag, A new approach for determining the length of intervals for fuzzy time series. Appl. Soft Comput. 9(2), 647–651 (2009)CrossRefGoogle Scholar
  14. 14.
    K. Huarng, T.H.-K. Yu, Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(2), 328–340 (2006)Google Scholar
  15. 15.
    R.-C. Tsaur et al., A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan and us dollar. Int. J. Innov. Comput. Inf. Control. 8(7), 4931–4942 (2012)Google Scholar
  16. 16.
    H. Guney, M.A. Bakir, C.H. Aladag, A novel stochastic seasonal fuzzy time series forecasting model. Int. J. Fuzzy Syst. 20(3), 729–740 (2018)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Q. Song, Seasonal forecasting in fuzzy time series. Fuzzy Sets Syst. 2(107), 235–236 (1999)MathSciNetCrossRefGoogle Scholar
  18. 18.
    E. Egrioglu, C.H. Aladag, U. Yolcu, M.A. Basaran, V.R. Uslu, A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. Expert Syst. Appl. 36(4), 7424–7434 (2009)CrossRefGoogle Scholar
  19. 19.
    S. Suhartono, M.H. Lee, A hybrid approach based on winters model and weighted fuzzy time series for forecasting trend and seasonal data. J. Math. Stat 7(3), 177–183 (2011)CrossRefGoogle Scholar
  20. 20.
    F.-M. Tseng, G.-H. Tzeng, A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst. 126(3), 367–376 (2002)MathSciNetCrossRefGoogle Scholar
  21. 21.
    G.P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)CrossRefGoogle Scholar
  22. 22.
    R.J. Hyndman, M. Akram, Time series data library (2010), http://robjhyndman.com/TSDL
  23. 23.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • A. J. Saleena
    • 1
  • C. Jessy John
    • 1
  1. 1.Department of MathematicsNational Institute of Technology CalicutKozhikodeIndia

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