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JACIII Vol.18 No.2 pp. 197-203
doi: 10.20965/jaciii.2014.p0197
(2014)

Paper:

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Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data

Yoshiyuki Yabuuchi* and Junzo Watada**

*Faculty of Economics, Shimonoseki City University, 2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan

**Graduate School of Information, Production and Systems, Waseda University, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0196, Japan

Received:
October 14, 2013
Accepted:
January 14, 2014
Published:
March 20, 2014
Creative Commons License
Keywords:
fuzzy time-series model, possibility, autocorrelation, fuzzy random variables, confidence intervals
Abstract
Economic analyses are typical methods based on timeseries data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random timeseries data. These confidence intervals play an essential role in dealing with fuzzy random data on the fuzzy autocorrelation model that we have presented. We analyze tick-by-tick data of stock transactions and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model that we propose in this paper.
Cite this article as:
Y. Yabuuchi and J. Watada, “Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.2, pp. 197-203, 2014.
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References
  1. [1] G.P. Box and G.M. Jenkins, “Time Series Analysis: Forecasting and Control,” Holden-day Inc., San Francisco, CA, 1976.
  2. [2] W. Vandaele, “Applied Time Series and Box-Jenkins Model,” Academic Press, 1983.
  3. [3] Y. Yabuuchi, J.Watada, and Y. Toyoura, “Fuzzy AR model of stock price,” Scientiae Mathematicae Japonicae, Vol.60, No.2, pp. 303-310, 2004.
  4. [4] H. Tanaka and J. Watada, “Possibilistic Linear Systems and Their Application to The Linear Regression Model,” Fuzzy Sets and Systems, Vol.27, pp. 275-289, 1988.
  5. [5] J. Watada, “Fuzzy time series analysis and forecasting of sales volume,” edited by J. Kacprzyk and M. Fedrizzi, Fuzzy Regression Analysis, Omnitec Press, Warsaw and Physica-Verlag, Heidelberg, pp. 211-227, 1992.
  6. [6] J. Watada, “Possibilistic Time-series Analysis and Its Analysis of Consumption,” D. Dubois, H. Prade and R. R. Yager (Eds) , John Wiley & Sons, INC., pp. 187-217, 1996.
  7. [7] K. Ozawa, T. Watanabe, and M. Kanke, “Forecasting Fuzzy Time Series with Fuzzy AR Model,” Proc. of the 20th Int. Conf. on Computers & Industrial Engineering, pp. 105-108, 1996.
  8. [8] F.M. Tseng, G.H. Tzeng, H.C. Yu, and B.J.C. Yuan, “Fuzzy ARIMAModel for forecasting the foreign exchange market,” Fuzzy Sets and Systems, Vol.118, No.1, pp. 9-19, 2001.
  9. [9] F.M. Tseng and G.H. Tzeng, “A fuzzy seasonal ARIMA model for forecasting,” Fuzzy Sets and Systems, Vol.126, No.3, pp. 367-376, 2002.
  10. [10] Q. Song and B.S. Chissom, “Forecasting enrollments with fuzzy time series – Part I,” Fuzzy Sets and Systems, Vol.54, No.1, pp. 1-9, 1993.
  11. [11] Q. Song and B.S. Chissom, “Fuzzy times series and its models,” Fuzzy Sets and Systems, Vol.54, No.3, pp. 269-277, 1993.
  12. [12] Q. Song and B.S. Chissom, “Forecasting enrollments with fuzzy time series – Part II,” Fuzzy Sets and Systems, Vol.62, No.1, pp. 1-8, 1994.
  13. [13] H.K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” Phisica A: Statistical Mechanics and its Applications, Vol.349, No.3-4, pp. 609-624, 2005.
  14. [14] Y.C. Cheng, S.T. Li, H.Y. Li, and P.C. Chen, “A Novel Forecasting Model Based on Similarity Measure for Fuzzy Time Series,” Proc. of Business and Information, Vol.8, 2011.
  15. [15] H.J. Teoh, C.H. Cheng, H.H. Chu, and J.S. Chen, “Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets,” Data & Knowledge Engineering, Vol.67, pp. 103-117, 2008.
  16. [16] S.M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets and Systems, Vol.81, No.3, pp. 311-319, 1996.
  17. [17] Z. Ismail and R. Efendi, “Enrollment Forecasting based on Modified Weight Fuzzy Time Series,” J. of Artificial Intelligence, Vol.4, No.1, pp. 110-118, 2011.
  18. [18] R. Dong and W. Pedrycz, “A granular time series approach to longterm forecasting and trend forecasting,” Physica A: Statistical Mechanics and its Applications, Vol.387, No.13, pp. 3253-3270, 2008.
  19. [19] C.H.L. Lee, A. Liu, and W.S. Chen, “Pattern discovery of fuzzy time series for financial prediction,” IEEE Trans. on Knowledge and Data Engineering, Vol.18, Issue 5, pp. 613-625, 2006.
  20. [20] M. Khashei, M. Bijari, and G.A.R. Ardali, “Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs),” Neurocomputing, Vol.72, No.4-6, pp. 956-967, 2009.
  21. [21] K. Lukoseviciute and M. Ragulskis, “Evolutionary algorithms for the selection of time lags for time series forecasting by fuzzy inference systems,” Neurocomputing, Vol.73, No.10-12, pp. 2077-2088, 2010.
  22. [22] T. Partal and Ö. Kişi, “Wavelet and neuro-fuzzy conjunction model for precipitation forecasting,” J. of Hydrology, Vol.342, No.1-2, pp. 199-212, 2007.
  23. [23] O. Valenzuela, I. Rojas, F. Rojas, H. Pomares, L.J. Herrera, A. Guillen, L. Marquez, and M. Pasadas, “Hybridization of intelligent techniques and ARIMA models for time series prediction,” Fuzzy Sets and Systems, Vol.159, No.7, pp. 821-845, 2008.
  24. [24] J.T Yao and J.P. Herbert, “Financial time-series analysis with rough sets,” Applied Soft Computing, Vol.9, No.3, pp. 1000-1007, 2009.
  25. [25] I. Couso, D. Dubois, S. Montes, and L. Sánchez, “On various definitions of the various of a fuzzy random variable,” Proc. of the 5th Int. Symp. on Imprecise Probabilities: Theories and Applications, pp. 135-144, 2007.
  26. [26] N. Watanabe, “Fuzzy Random Variables and Statistical Inference,” Japan Society for Fuzzy Theory and Systems, Vol.8, No.5, pp. 908-917, 1996.
  27. [27] H. Kwakernaak, “Fuzzy random variables-I. definitions and theorems,” Information Sciences, Vol.15, No.1, pp. 1-29, 1978.
  28. [28] H. Kwakernaak, “Fuzzy random variables-II. Algorithms and examples for the discrete case,” Information Sciences, Vol.17, No.3, pp. 253-278, 1979.
  29. [29] M. L. Puri and D. A. Ralescu, “The Concept of Normality for Fuzzy Random Variables,” Ann. Probab., Vol.13, No.4, pp. 1373-1379, 1985.
  30. [30] J. Watada, S. Wang, and W. Pedrycz, “Building Confidence-Interval-Based Fuzzy Random Regression Models,” IEEE Trans. on Fuzzy Systems, Vol.17, No.6, pp. 1273-1283, 2009.

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