Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations

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Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. / Aladag, Cagdas H.; Basaran, Murat A.; Egrioglu, Erol et al.
In: Expert Systems with Applications, Vol. 36, No. 3 PART 1, 01.01.2009, p. 4228-4231.

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

Harvard

Aladag, CH, Basaran, MA, Egrioglu, E, Yolcu, U & Uslu, VR 2009, 'Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations', Expert Systems with Applications, vol. 36, no. 3 PART 1, pp. 4228-4231. https://doi.org/10.1016/j.eswa.2008.04.001

APA

Aladag, C. H., Basaran, M. A., Egrioglu, E., Yolcu, U., & Uslu, V. R. (2009). Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Systems with Applications, 36(3 PART 1), 4228-4231. https://doi.org/10.1016/j.eswa.2008.04.001

Vancouver

Aladag CH, Basaran MA, Egrioglu E, Yolcu U, Uslu VR. Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Systems with Applications. 2009 Jan 1;36(3 PART 1):4228-4231. doi: 10.1016/j.eswa.2008.04.001

Author

Aladag, Cagdas H. ; Basaran, Murat A. ; Egrioglu, Erol et al. / Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. In: Expert Systems with Applications. 2009 ; Vol. 36, No. 3 PART 1. pp. 4228-4231.

Bibtex

@article{cdd76cad0f644255b34acd5c4e95caf2,

title = "Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations",

abstract = "A given observation in time series does not only depend on preceding one but also previous ones in general. Therefore, high order fuzzy time series approach might obtain better forecasts than does first order fuzzy time series approach. Defining fuzzy relation in high order fuzzy time series approach are more complicated than that in first order fuzzy time series approach. A new proposed approach, which uses feed forward neural networks to define fuzzy relation in high order fuzzy time series, is introduced in this paper. The new proposed approach is applied to well-known enrollment data for the University of Alabama and obtained results are compared with other methods proposed in the literature. It is found that the proposed method produces better forecasts than the other methods.",

keywords = "Forecasting, Fuzzy relation, Fuzzy set, High order fuzzy time series, Neural networks",

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

year = "2009",

month = jan,

day = "1",

doi = "10.1016/j.eswa.2008.04.001",

language = "English",

volume = "36",

pages = "4228--4231",

journal = "Expert Systems with Applications",

issn = "0957-4174",

publisher = "Elsevier Ltd",

number = "3 PART 1",

}

RIS

TY - JOUR

T1 - Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations

AU - Aladag, Cagdas H.

AU - Basaran, Murat A.

AU - Egrioglu, Erol

AU - Yolcu, Ufuk

AU - Uslu, Vedide R.

PY - 2009/1/1

Y1 - 2009/1/1

N2 - A given observation in time series does not only depend on preceding one but also previous ones in general. Therefore, high order fuzzy time series approach might obtain better forecasts than does first order fuzzy time series approach. Defining fuzzy relation in high order fuzzy time series approach are more complicated than that in first order fuzzy time series approach. A new proposed approach, which uses feed forward neural networks to define fuzzy relation in high order fuzzy time series, is introduced in this paper. The new proposed approach is applied to well-known enrollment data for the University of Alabama and obtained results are compared with other methods proposed in the literature. It is found that the proposed method produces better forecasts than the other methods.

AB - A given observation in time series does not only depend on preceding one but also previous ones in general. Therefore, high order fuzzy time series approach might obtain better forecasts than does first order fuzzy time series approach. Defining fuzzy relation in high order fuzzy time series approach are more complicated than that in first order fuzzy time series approach. A new proposed approach, which uses feed forward neural networks to define fuzzy relation in high order fuzzy time series, is introduced in this paper. The new proposed approach is applied to well-known enrollment data for the University of Alabama and obtained results are compared with other methods proposed in the literature. It is found that the proposed method produces better forecasts than the other methods.

KW - Forecasting

KW - Fuzzy relation

KW - Fuzzy set

KW - High order fuzzy time series

KW - Neural networks

U2 - 10.1016/j.eswa.2008.04.001

DO - 10.1016/j.eswa.2008.04.001

M3 - Journal article

AN - SCOPUS:58349090456

VL - 36

SP - 4228

EP - 4231

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 3 PART 1

ER -

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