Fuzzy Semiparametric Support Vector Regression for Seasonal Time Series Analysis

Title & Authors
Fuzzy Semiparametric Support Vector Regression for Seasonal Time Series Analysis
Shim, Joo-Yong; Hwang, Chang-Ha; Hong, Dug-Hun;

Abstract
Fuzzy regression is used as a complement or an alternative to represent the relation between variables among the forecasting models especially when the data is insufficient to evaluate the relation. Such phenomenon often occurs in seasonal time series data which require large amount of data to describe the underlying pattern. Semiparametric model is useful tool in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. In this paper we propose fuzzy semiparametric support vector regression so that it can provide good performance on forecasting of the seasonal time series by incorporating into fuzzy support vector regression the basis functions which indicate the seasonal variation of time series. In order to indicate the performance of this method, we present two examples of predicting the seasonal time series. Experimental results show that the proposed method is very attractive for the seasonal time series in fuzzy environments.
Keywords
Fuzzy regression;seasonal time series;semiparametric model;support vector regression;
Language
English
Cited by
1.
서포트벡터기계를 이용한 VaR 모형의 결합,김용태;심주용;이장택;황창하;

Communications for Statistical Applications and Methods, 2009. vol.16. 5, pp.791-801
2.
Hybrid Fuzzy Least Squares Support Vector Machine Regression for Crisp Input and Fuzzy Output,;;;

Communications for Statistical Applications and Methods, 2010. vol.17. 2, pp.141-151
References
1.
Box, G. P. and Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco

2.
Burman, P. and Shumway, R. (1998). Semiparametric modeling of seasonal time series, Journal of Time Series Analysis, 19, 127-145

3.
Chang, P. T. (1997). Fuzzy seasonality forecasting, Fuzzy Sets and Systems, 90, 1-10

4.
Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems, 81, 311-319

5.
Cherkassky, V., Shao, X., Muller, F. M. and Vapnik, V. N. (1999). Model complexity control for regression using VC generalization bounds, IEEE Transactions on Neural Networks, 10, 1075-1089

6.
Dominici, F., McDermott, A. and Hastie, T. J. (2004). Improved semi-parametric time series models of air pollution and mortality, Journal of the American Statistical Association, 99, 938-948

7.
Franses, P. H. (1998). Time Series Models for Business and Economic Forecasting, Cambridge Uni-versity Press, Cambridge

8.
Ghysels, E. and Osborn, D. R. (2001). The Econometric Analysis of Seasonal Time Series, Cambridge University Press, Cambridge

9.
Haykin, S. and Kosko, B. (2001). Intelligent Signal Processing, Wiley-IEEE Press, New York

10.
Hong, D. H. and Hwang, C. (2005). Interval regression analysis using quadratic loss support vector machine, IEEE Transactions on Fuzzy Systems, 13, 229-237

11.
Hwang, C., Hong, D. H. and Seok, K. H. (2006). Support vector interval regression machine for crisp input and output data, Fuzzy Sets and Systems, 157, 1114-1125

12.
Hwang, J. R., Chen, S. M. and Lee, C. H. (1998). Handling forecasting problems using fuzzy time series, Fuzzy Sets and Systems, 100, 217-228

13.
Montgomery, D. C., Johson, L. A. and Gardiner, J. S. (1990). Forecasting and Time Series Analysis, McGraw-Hill, New York

14.
Smola, A. J., $Frie\beta$, T. T. and Scholkopf, B. (1998). Semiparametric support vector and linear pro-gramming machines, In Proceedings of the 1998 conference on Advances in Neural Information Processing Systems, 585-591

15.
Song, Q. and Chissom, B. S. (1993a). Fuzzy time-series and its models, Fuzzy Sets and Systems, 54, 269-277

16.
Song, Q. and Chissom, B. S. (1993b). Forecasting enrollments with fuzzy time series - part Ⅰ, Fuzzy Sets and Systems, 54, 1-9

17.
Song, Q. and Chissom, B. S. (1994). Forecasting enrollmenis with fuzzy time series - part Ⅱ, Fuzzy Sets and Systems, 62, 1-8

18.
Song, Q., Leland, R. P. and Chissom, B. S. (1995). A new fuzzy time-series model of fuzzy number observations, Fuzzy Sets and Systems, 73, 341-348

19.
Tanaka, H. (1987). Fuzzy data analysis by possibility linear models, Fuzzy Sets and Systems, 24, 363-375

20.
Tanaka, H. and Ishibuchi, H. (1992). Possibility regression analysis based on linear programming, Fuzzy Regression Analysis, 47-60

21.
Tanaka, H. and Lee, H. (1998). Interval regression analysis by quadratic programming approach, IEEE Transactions on Fuzzy Systems, 6, 473-481

22.
Tanaka, H., Uejima, S. and Asai, K. (1982). Linear regression analysis with fuzzy model, IEEE Transactions on Systems Man Cybernetics, 12, 903-907

23.
Tsaur, R. C., Wang, H. F. and Yang, J. C. O. (2002). Fuzzy regression for seasonal time series analysis, International Journal of Information Technology & Decision Making, 1, 165-175

24.
Tseng,F. M. and Tzeng, G. H. (2002). A fuzzy seasonal ARIMA model for forecasting, Fuzzy Sets and Systems, 126, 367-376

25.
Tseng, F. M., Tzeng, G. H., Yu, H. C. and Yuan, B. J. C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market, Fuzzy Sets and Systems, 118, 9-19

26.
Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer, Berlin

27.
Vapnik, V. (1998). Statistical Learning Theory, John Wiley & Sons, New York

28.
Watada, J. (1992). Fuzzy time series analysis and forecasting of sales volume, Fuzzy Regression Analysis, Omnitech Press, Warsaw and Physica-Verlag, Heidelberg, 211-227

29.
West, M. and Harrison, P. J. (1997). Bayesian Forecasting and Dynamic Models, Springer-Verlag, New York

30.
Zadeh, L. A. (1965). Fuzzy sets, Information and Control, 8, 338-353