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High-Order Fuzzy-Neuro Time Series Forecasting Model

  • Pritpal SinghEmail author
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 330)

Abstract

In this chapter, we present a new model based on hybridization of FTS theory with ANN. In FTS models, lengths of intervals always affect the results of forecasting. So, for creating the effective lengths of intervals of the historical time series data set, a new “Re-Partitioning Discretization (RPD)” approach is introduced in the proposed model. Many researchers suggest that high-order fuzzy relationships improve the forecasting accuracy of the models. Therefore, in this study, we use the high-order fuzzy relationships in order to obtain more accurate forecasting results. Most of the FTS models use the current state’s fuzzified values to obtain the forecasting results. The utilization of current state’s fuzzified values (right hand side fuzzy relations) for prediction degrades the predictive skill of the FTS models, because predicted values lie within the sample. Therefore, for advance forecasting of time series, previous state’s fuzzified values (left hand side of fuzzy relations) are employed in the proposed model. To defuzzify these fuzzified time series values, an ANN based architecture is developed, and incorporated in the proposed model.

Keywords

FTS High-order Temperature Stock exchange Interval FLR ANN 

References

  1. Aladag CH, Basaran MA, Egrioglu E, Yolcu U, Uslu VR (2009) Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Syst Appl 36(3):4228–4231CrossRefGoogle Scholar
  2. Chang PC, Liu CH, Fan CY (2009) Data clustering and fuzzy neural network for sales forecasting: a case study in printed circuit board industry. Knowl-Based Syst 22(5):344–355Google Scholar
  3. Chen SM (2002) Forecasting enrollments based on high-order fuzzy time series. Cybern Syst Int J 33(1):1–16CrossRefzbMATHGoogle Scholar
  4. Chen SM, Chen CD (2011a) Handling forecasting problems based on high-order fuzzy logical relationships. Expert Syst Appl 38(4):3857–3864CrossRefGoogle Scholar
  5. Chen SM, Chen CD (2011b) Handling forecasting problems based on high-order fuzzy logical relationships. Expert Syst Appl 38(4):3857–3864CrossRefGoogle Scholar
  6. Chen SM, Hwang JR (2000) Temperature prediction using fuzzy time series. IEEE Trans Syst Man Cybern Part B Cybern 30:263–275CrossRefGoogle Scholar
  7. Chen TL, Cheng CH, Teoh HJ (2008) High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets. Phys A Stat Mech Appl 387(4):876–888CrossRefGoogle Scholar
  8. Cheng CH, Chen TL, Wei LY (2010) A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inf Sci 180(9):1610–1629CrossRefGoogle Scholar
  9. Hadavandi E, Shavandi H, Ghanbari A (2010) Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowl-Based Syst 23(8):800–808CrossRefGoogle Scholar
  10. Han J, Kamber M (2001) Data Mining Concepts Tech, 1st edn. Morgan Kaufmann Publishers, USAGoogle Scholar
  11. Huang YL, Horng SJ, He M, Fan P, Kao TW, Khan MK, Lai JL, Kuo IH (2011) A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization. Expert Syst Appl 38(7):8014–8023CrossRefGoogle Scholar
  12. Huarng K, Yu THK (2006) The application of neural networks to forecast fuzzy time series. Phys A Stat Mech Appl 363(2):481–491CrossRefGoogle Scholar
  13. Keles A, Kolcak M, Keles A (2008) The adaptive neuro-fuzzy model for forecasting the domestic debt. Knowl-Based Syst 21(8):951–957CrossRefGoogle Scholar
  14. Kuo IH, Horng SJ, Kao TW, Lin TL, Lee CL, Pan Y (2009) An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Syst Appl 36(3, Part 2):6108–6117Google Scholar
  15. Kuo IH, Horng SJ, Chen YH, Run RS, Kao TW, Chen RJ, Lai JL, Lin TL (2010) Forecasting TAIFEX based on fuzzy time series and particle swarm optimization. Expert Syst Appl 37(2):1494–1502CrossRefGoogle Scholar
  16. Pal SK, Mitra P (2004) Case generation using rough sets with fuzzy representation. IEEE Trans Knowl Data Eng 16(3):292–300CrossRefGoogle Scholar
  17. Roy S, Chakraborty U (2013) Introduction to soft computing. Dorling Kindersley (India) Pvt Ltd, New DelhiGoogle Scholar
  18. Song Q, Chissom BS (1993a) Forecasting enrollments with fuzzy time series—Part I. Fuzzy Sets Syst 54(1):1–9CrossRefGoogle Scholar
  19. Song Q, Chissom BS (1993b) Fuzzy time series and its models. Fuzzy Sets Syst 54(1):1–9MathSciNetCrossRefzbMATHGoogle Scholar
  20. Song Q, Chissom BS (1994) Forecasting enrollments with fuzzy time series—Part II. Fuzzy Sets Syst 62(1):1–8CrossRefGoogle Scholar
  21. Sturges H (1926) The choice of a class-interval. J Am Stat Assoc 21:65–66CrossRefGoogle Scholar
  22. Teoh HJ, Cheng CH, Chu HH, Chen JS (2008) Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets. Data Knowl Eng 67(1):103–117CrossRefGoogle Scholar
  23. Tsai CC, Wu SJ (2000) Forecasting enrolments with high-order fuzzy time series. In: 19th international conference of the North American. Fuzzy Information Processing Society, Atlanta, GA, pp 196–200Google Scholar
  24. Wilson ID, Paris SD, Ware JA, Jenkins DH (2002) Residential property price time series forecasting with neural networks. Knowl-Based Syst 15(5–6):335–341CrossRefGoogle Scholar
  25. Yang L, Dawson CW, Brown MR, Gell M (2006) Neural network and GA approaches for dwelling fire occurrence prediction. Knowl-Based Syst 19(4):213–219Google Scholar
  26. Yu THK, Huarng KH (2008) A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst Appl 34(4):2945–2952CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThapar UniversityPatialaIndia

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