Finding an optimal interval length in high order fuzzy time series

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

Published

Standard

Finding an optimal interval length in high order fuzzy time series. / Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk et al.
In: Expert Systems with Applications, Vol. 37, No. 7, 01.07.2010, p. 5052-5055.

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

Harvard

Egrioglu, E, Aladag, CH, Yolcu, U, Uslu, VR & Basaran, MA 2010, 'Finding an optimal interval length in high order fuzzy time series', Expert Systems with Applications, vol. 37, no. 7, pp. 5052-5055. https://doi.org/10.1016/j.eswa.2009.12.006

APA

Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R., & Basaran, M. A. (2010). Finding an optimal interval length in high order fuzzy time series. Expert Systems with Applications, 37(7), 5052-5055. https://doi.org/10.1016/j.eswa.2009.12.006

Vancouver

Egrioglu E, Aladag CH, Yolcu U, Uslu VR, Basaran MA. Finding an optimal interval length in high order fuzzy time series. Expert Systems with Applications. 2010 Jul 1;37(7):5052-5055. doi: 10.1016/j.eswa.2009.12.006

Author

Egrioglu, Erol ; Aladag, Cagdas Hakan ; Yolcu, Ufuk et al. / Finding an optimal interval length in high order fuzzy time series. In: Expert Systems with Applications. 2010 ; Vol. 37, No. 7. pp. 5052-5055.

Bibtex

@article{0dc859d1c0c44b70abd9e7bad8cfd167,

title = "Finding an optimal interval length in high order fuzzy time series",

abstract = "Univariate fuzzy time series approaches which have been widely used in recent years can be divided into two classes, which are called first order and high order models. In the literature, it has been shown that high order fuzzy time series approaches improve the forecasting accuracy. One of the important parts of obtaining high accuracy forecasts in fuzzy time series is that the length of interval is very vital. As mentioned in the first-order models by Egrioglu, Aladag, Basaran, Uslu, and Yolcu (2009), the length of interval also plays very important role in high order models too. In this study, a new approach which uses an optimization technique with a single-variable constraint is proposed to determine an optimal interval length in high order fuzzy time series models. An optimization procedure is used in order to determine optimum length of interval for the best forecasting accuracy, we used optimization procedure. In the optimization process, we used a MATLAB function employing an algorithm based on golden section search and parabolic interpolation. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results.",

keywords = "Forecasting, Fuzzy sets, High order fuzzy time series forecasting model, Length of interval, Optimization",

author = "Erol Egrioglu and Aladag, {Cagdas Hakan} and Ufuk Yolcu and Uslu, {Vedide R.} and Basaran, {Murat A.}",

year = "2010",

month = jul,

day = "1",

doi = "10.1016/j.eswa.2009.12.006",

language = "English",

volume = "37",

pages = "5052--5055",

journal = "Expert Systems with Applications",

issn = "0957-4174",

publisher = "Elsevier Ltd",

number = "7",

}

RIS

TY - JOUR

T1 - Finding an optimal interval length in high order fuzzy time series

AU - Egrioglu, Erol

AU - Aladag, Cagdas Hakan

AU - Yolcu, Ufuk

AU - Uslu, Vedide R.

AU - Basaran, Murat A.

PY - 2010/7/1

Y1 - 2010/7/1

N2 - Univariate fuzzy time series approaches which have been widely used in recent years can be divided into two classes, which are called first order and high order models. In the literature, it has been shown that high order fuzzy time series approaches improve the forecasting accuracy. One of the important parts of obtaining high accuracy forecasts in fuzzy time series is that the length of interval is very vital. As mentioned in the first-order models by Egrioglu, Aladag, Basaran, Uslu, and Yolcu (2009), the length of interval also plays very important role in high order models too. In this study, a new approach which uses an optimization technique with a single-variable constraint is proposed to determine an optimal interval length in high order fuzzy time series models. An optimization procedure is used in order to determine optimum length of interval for the best forecasting accuracy, we used optimization procedure. In the optimization process, we used a MATLAB function employing an algorithm based on golden section search and parabolic interpolation. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results.

AB - Univariate fuzzy time series approaches which have been widely used in recent years can be divided into two classes, which are called first order and high order models. In the literature, it has been shown that high order fuzzy time series approaches improve the forecasting accuracy. One of the important parts of obtaining high accuracy forecasts in fuzzy time series is that the length of interval is very vital. As mentioned in the first-order models by Egrioglu, Aladag, Basaran, Uslu, and Yolcu (2009), the length of interval also plays very important role in high order models too. In this study, a new approach which uses an optimization technique with a single-variable constraint is proposed to determine an optimal interval length in high order fuzzy time series models. An optimization procedure is used in order to determine optimum length of interval for the best forecasting accuracy, we used optimization procedure. In the optimization process, we used a MATLAB function employing an algorithm based on golden section search and parabolic interpolation. 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 - High order fuzzy time series forecasting model

KW - Length of interval

KW - Optimization

U2 - 10.1016/j.eswa.2009.12.006

DO - 10.1016/j.eswa.2009.12.006

M3 - Journal article

AN - SCOPUS:77950189383

VL - 37

SP - 5052

EP - 5055

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 7

ER -

Research

Associated organisational unit

Links

Text available via DOI:

Keywords