A New Bandwidth Interval Based Forecasting Method for Enrollments Using Fuzzy Time Series
Hemant Kumar Pathak, Prachi Singh
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DOI: 10.4236/am.2011.24065   PDF    HTML     6,203 Downloads   11,042 Views   Citations

Abstract

In this paper, we introduce the concept of (4/3)? bandwidth interval based forecasting. The historical enrollments of the university of Alabama are used to illustrate the proposed method. In this paper we use the new simplified technique to find the fuzzy logical relations.

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H. Pathak and P. Singh, "A New Bandwidth Interval Based Forecasting Method for Enrollments Using Fuzzy Time Series," Applied Mathematics, Vol. 2 No. 4, 2011, pp. 504-507. doi: 10.4236/am.2011.24065.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] L. A. Zadeh, “Fuzzy Sets,” Information Control, Vol. 8, No. 3, 1965, pp. 338-353.
[2] Q. Song and B. S. Chissom, “Forecasting Enrollment with Fuzzy Time Series-Part I,” Fuzzy Sets and Systems, Vol. 54, No. 1, 1993, pp. 1-9. doi:10.1016/0165-0114(93)90355-L
[3] S. M. Chen, “Forecasting Enrollments Based on Fuzzy Time Series,” Fuzzy Sets and Systems, Vol. 81, No. 3, 1996, pp. 311-319. doi:10.1016/0165-0114(95)00220-0
[4] S. M. Chen, “Forecasting Enrollments Based on High Order Fuzzy Time Series,” Cybernetics and Systems: An International Journal, Vol. l33, No. 1, 2002, pp. 1-16.
[5] I. H. Kuo, S. J. Horng, T. W. Kao, C. L. Lee, T. L. Lin and Y. Pan, “An Improved Method for Forecasting Enrollment Based on Fuzzy Time Series and Particle Swarm Optimization,” Expert Systems with Applications, Vol. 36, No. 3, 2009, pp. 311-319.
[6] S. M. Chen and C. C. Hsu, “A New Method to Forecast Enrollment Using Fuzzy Time Series,” International Journal of Applied Science and Engineering, Vol. 3, No. 2, 2004, pp. 234-244.
[7] M. H. Lee, R. Efendi and Z. Ismail, “Modified Weighted for Enrollment Forecasting Based of Fuzzy Time Series,” Matematika, Vol. 25, No. 1, 2009, pp. 67-78.
[8] C. H. Cheng, T. L. Chen and C. H. Chiang, “Trend-Weighted Fuzzy Time Series Model for TAIEX Forecasting,” Proceedings of the 13th International Conference on Neural Information Processing, Part-III, Lecture Notes in Computer Science, Hong Kong, Vol. 4234, 3-6 October 2006, pp. 469-477.
[9] Q. Song and B. S. Chissom, “Fuzzy Time Series and Its Models,” Fuzzy Sets and Systems, Vol. 54, No. 3, 1993, pp. 267-277.doi:10.1016/0165-0114(93)90372-O
[10] S. M. Chen and J. R. Hwang, “Temperature Prediction Using Fuzzy Time Series,” IEEE Transactions on Systems, Man and Cybernatics-Part B: Cybrnetics, Vol. 30, No. 2, 2000, pp. 263-275.
[11] K. Huarng, “Effective Lengths of Intervals to Improve Forecasting in Fuzzy Time Series,” Fuzzy Sets and Systems, Vol. 123, No. 3, 2001, pp. 387-394. doi:10.1016/S0165-0114(00)00057-9

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