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Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

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Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. / Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk.
In: Expert Systems with Applications, Vol. 40, No. 3, 15.02.2013, p. 854-857.

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

Harvard

Egrioglu, E, Aladag, CH & Yolcu, U 2013, 'Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks', Expert Systems with Applications, vol. 40, no. 3, pp. 854-857. https://doi.org/10.1016/j.eswa.2012.05.040

APA

Egrioglu, E., Aladag, C. H., & Yolcu, U. (2013). Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Systems with Applications, 40(3), 854-857. https://doi.org/10.1016/j.eswa.2012.05.040

Vancouver

Egrioglu E, Aladag CH, Yolcu U. Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Systems with Applications. 2013 Feb 15;40(3):854-857. doi: 10.1016/j.eswa.2012.05.040

Author

Egrioglu, Erol ; Aladag, Cagdas Hakan ; Yolcu, Ufuk. / Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. In: Expert Systems with Applications. 2013 ; Vol. 40, No. 3. pp. 854-857.

Bibtex

@article{cf647f8b7e0644dbaf72ff0cb2aa9f0f,
title = "Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks",
abstract = "In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural networks and genetic algorithms have been used in fuzzy time series method to improve the method. While fuzzy clustering and genetic algorithms are being used for fuzzification, artificial neural networks method is being preferred for using in defining fuzzy relationships. In this study, a hybrid fuzzy time series approach is proposed to reach more accurate forecasts. In the proposed hybrid approach, fuzzy c-means clustering method and artificial neural networks are employed for fuzzification and defining fuzzy relationships, respectively. The enrollment data of University of Alabama is forecasted by using both the proposed method and the other fuzzy time series approaches. As a result of comparison, it is seen that the most accurate forecasts are obtained when the proposed hybrid fuzzy time series approach is used.",
keywords = "Artificial neural networks, Defuzzification, Forecast, Fuzzification, Fuzzy c-means, Fuzzy time series",
author = "Erol Egrioglu and Aladag, {Cagdas Hakan} and Ufuk Yolcu",
year = "2013",
month = feb,
day = "15",
doi = "10.1016/j.eswa.2012.05.040",
language = "English",
volume = "40",
pages = "854--857",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

AU - Egrioglu, Erol

AU - Aladag, Cagdas Hakan

AU - Yolcu, Ufuk

PY - 2013/2/15

Y1 - 2013/2/15

N2 - In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural networks and genetic algorithms have been used in fuzzy time series method to improve the method. While fuzzy clustering and genetic algorithms are being used for fuzzification, artificial neural networks method is being preferred for using in defining fuzzy relationships. In this study, a hybrid fuzzy time series approach is proposed to reach more accurate forecasts. In the proposed hybrid approach, fuzzy c-means clustering method and artificial neural networks are employed for fuzzification and defining fuzzy relationships, respectively. The enrollment data of University of Alabama is forecasted by using both the proposed method and the other fuzzy time series approaches. As a result of comparison, it is seen that the most accurate forecasts are obtained when the proposed hybrid fuzzy time series approach is used.

AB - In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural networks and genetic algorithms have been used in fuzzy time series method to improve the method. While fuzzy clustering and genetic algorithms are being used for fuzzification, artificial neural networks method is being preferred for using in defining fuzzy relationships. In this study, a hybrid fuzzy time series approach is proposed to reach more accurate forecasts. In the proposed hybrid approach, fuzzy c-means clustering method and artificial neural networks are employed for fuzzification and defining fuzzy relationships, respectively. The enrollment data of University of Alabama is forecasted by using both the proposed method and the other fuzzy time series approaches. As a result of comparison, it is seen that the most accurate forecasts are obtained when the proposed hybrid fuzzy time series approach is used.

KW - Artificial neural networks

KW - Defuzzification

KW - Forecast

KW - Fuzzification

KW - Fuzzy c-means

KW - Fuzzy time series

U2 - 10.1016/j.eswa.2012.05.040

DO - 10.1016/j.eswa.2012.05.040

M3 - Journal article

AN - SCOPUS:84867844623

VL - 40

SP - 854

EP - 857

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 3

ER -