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A new approach based on the optimization of the length of intervals in fuzzy time series

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A new approach based on the optimization of the length of intervals in fuzzy time series. / Egrioglu, Erol; Aladag, Cagdas Hakan; Basaran, Murat A. et al.
In: Journal of Intelligent and Fuzzy Systems, Vol. 22, No. 1, 17.01.2011, p. 15-19.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Egrioglu, E, Aladag, CH, Basaran, MA, Yolcu, U & Uslu, VR 2011, 'A new approach based on the optimization of the length of intervals in fuzzy time series', Journal of Intelligent and Fuzzy Systems, vol. 22, no. 1, pp. 15-19. https://doi.org/10.3233/IFS-2010-0470

APA

Egrioglu, E., Aladag, C. H., Basaran, M. A., Yolcu, U., & Uslu, V. R. (2011). A new approach based on the optimization of the length of intervals in fuzzy time series. Journal of Intelligent and Fuzzy Systems, 22(1), 15-19. https://doi.org/10.3233/IFS-2010-0470

Vancouver

Egrioglu E, Aladag CH, Basaran MA, Yolcu U, Uslu VR. A new approach based on the optimization of the length of intervals in fuzzy time series. Journal of Intelligent and Fuzzy Systems. 2011 Jan 17;22(1):15-19. doi: 10.3233/IFS-2010-0470

Author

Egrioglu, Erol ; Aladag, Cagdas Hakan ; Basaran, Murat A. et al. / A new approach based on the optimization of the length of intervals in fuzzy time series. In: Journal of Intelligent and Fuzzy Systems. 2011 ; Vol. 22, No. 1. pp. 15-19.

Bibtex

@article{1f61663332184e11ae5cb0b1587f3311,
title = "A new approach based on the optimization of the length of intervals in fuzzy time series",
abstract = "In fuzzy time series analysis, the determination of the interval length is an important issue. In many researches recently done, the length of intervals has been intuitively determined. In order to efficiently determine the length of intervals, two approaches which are based on the average and the distribution have been proposed by Huarng [4]. In this paper, we propose a new method based on the use of a single variable constrained optimization to determine the length of interval. In order to determine optimum length of interval for the best forecasting accuracy, we used a MATLAB function which is employing an algorithm based on golden section search and parabolic interpolation. Mean square error is used as a measure of forecasting accuracy so the objective function value is mean square error value for forecasted observations. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results.",
keywords = "Forecasting, fuzzy sets, fuzzy time series, length of interval, optimization",
author = "Erol Egrioglu and Aladag, {Cagdas Hakan} and Basaran, {Murat A.} and Ufuk Yolcu and Uslu, {Vedide R.}",
year = "2011",
month = jan,
day = "17",
doi = "10.3233/IFS-2010-0470",
language = "English",
volume = "22",
pages = "15--19",
journal = "Journal of Intelligent and Fuzzy Systems",
issn = "1064-1246",
publisher = "IOS Press",
number = "1",

}

RIS

TY - JOUR

T1 - A new approach based on the optimization of the length of intervals in fuzzy time series

AU - Egrioglu, Erol

AU - Aladag, Cagdas Hakan

AU - Basaran, Murat A.

AU - Yolcu, Ufuk

AU - Uslu, Vedide R.

PY - 2011/1/17

Y1 - 2011/1/17

N2 - In fuzzy time series analysis, the determination of the interval length is an important issue. In many researches recently done, the length of intervals has been intuitively determined. In order to efficiently determine the length of intervals, two approaches which are based on the average and the distribution have been proposed by Huarng [4]. In this paper, we propose a new method based on the use of a single variable constrained optimization to determine the length of interval. In order to determine optimum length of interval for the best forecasting accuracy, we used a MATLAB function which is employing an algorithm based on golden section search and parabolic interpolation. Mean square error is used as a measure of forecasting accuracy so the objective function value is mean square error value for forecasted observations. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results.

AB - In fuzzy time series analysis, the determination of the interval length is an important issue. In many researches recently done, the length of intervals has been intuitively determined. In order to efficiently determine the length of intervals, two approaches which are based on the average and the distribution have been proposed by Huarng [4]. In this paper, we propose a new method based on the use of a single variable constrained optimization to determine the length of interval. In order to determine optimum length of interval for the best forecasting accuracy, we used a MATLAB function which is employing an algorithm based on golden section search and parabolic interpolation. Mean square error is used as a measure of forecasting accuracy so the objective function value is mean square error value for forecasted observations. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results.

KW - Forecasting

KW - fuzzy sets

KW - fuzzy time series

KW - length of interval

KW - optimization

U2 - 10.3233/IFS-2010-0470

DO - 10.3233/IFS-2010-0470

M3 - Journal article

AN - SCOPUS:78651303153

VL - 22

SP - 15

EP - 19

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

SN - 1064-1246

IS - 1

ER -