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Fuzzy lagged variable selection in fuzzy time series with genetic algorithms

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Fuzzy lagged variable selection in fuzzy time series with genetic algorithms. / Aladag, Cagdas Hakan; Yolcu, Ufuk; Egrioglu, Erol et al.
In: Applied Soft Computing Journal, Vol. 22, 01.01.2014, p. 465-473.

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

Harvard

Aladag, CH, Yolcu, U, Egrioglu, E & Bas, E 2014, 'Fuzzy lagged variable selection in fuzzy time series with genetic algorithms', Applied Soft Computing Journal, vol. 22, pp. 465-473. https://doi.org/10.1016/j.asoc.2014.03.028

APA

Aladag, C. H., Yolcu, U., Egrioglu, E., & Bas, E. (2014). Fuzzy lagged variable selection in fuzzy time series with genetic algorithms. Applied Soft Computing Journal, 22, 465-473. https://doi.org/10.1016/j.asoc.2014.03.028

Vancouver

Aladag CH, Yolcu U, Egrioglu E, Bas E. Fuzzy lagged variable selection in fuzzy time series with genetic algorithms. Applied Soft Computing Journal. 2014 Jan 1;22:465-473. doi: 10.1016/j.asoc.2014.03.028

Author

Aladag, Cagdas Hakan ; Yolcu, Ufuk ; Egrioglu, Erol et al. / Fuzzy lagged variable selection in fuzzy time series with genetic algorithms. In: Applied Soft Computing Journal. 2014 ; Vol. 22. pp. 465-473.

Bibtex

@article{6cd5f9ec54c04ea39b22a6796c97d869,
title = "Fuzzy lagged variable selection in fuzzy time series with genetic algorithms",
abstract = "Fuzzy time series forecasting models can be divided into two subclasses which are first order and high order. In high order models, all lagged variables exist in the model according to the model order. Thus, some of these can exist in the model although these lagged variables are not significant in explaining fuzzy relationships. If such lagged variables can be removed from the model, fuzzy relationships will be defined better and it will cause more accurate forecasting results. In this study, a new fuzzy time series forecasting model has been proposed by defining a partial high order fuzzy time series forecasting model in which the selection of fuzzy lagged variables is done by using genetic algorithms. The proposed method is applied to some real life time series and obtained results are compared with those obtained from other methods available in the literature. It is shown that the proposed method has high forecasting accuracy.",
keywords = "Forecasting, Fuzzy time series, Genetic algorithms, Partial high order model, Variable selection",
author = "Aladag, {Cagdas Hakan} and Ufuk Yolcu and Erol Egrioglu and Eren Bas",
year = "2014",
month = jan,
day = "1",
doi = "10.1016/j.asoc.2014.03.028",
language = "English",
volume = "22",
pages = "465--473",
journal = "Applied Soft Computing Journal",
issn = "1568-4946",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Fuzzy lagged variable selection in fuzzy time series with genetic algorithms

AU - Aladag, Cagdas Hakan

AU - Yolcu, Ufuk

AU - Egrioglu, Erol

AU - Bas, Eren

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Fuzzy time series forecasting models can be divided into two subclasses which are first order and high order. In high order models, all lagged variables exist in the model according to the model order. Thus, some of these can exist in the model although these lagged variables are not significant in explaining fuzzy relationships. If such lagged variables can be removed from the model, fuzzy relationships will be defined better and it will cause more accurate forecasting results. In this study, a new fuzzy time series forecasting model has been proposed by defining a partial high order fuzzy time series forecasting model in which the selection of fuzzy lagged variables is done by using genetic algorithms. The proposed method is applied to some real life time series and obtained results are compared with those obtained from other methods available in the literature. It is shown that the proposed method has high forecasting accuracy.

AB - Fuzzy time series forecasting models can be divided into two subclasses which are first order and high order. In high order models, all lagged variables exist in the model according to the model order. Thus, some of these can exist in the model although these lagged variables are not significant in explaining fuzzy relationships. If such lagged variables can be removed from the model, fuzzy relationships will be defined better and it will cause more accurate forecasting results. In this study, a new fuzzy time series forecasting model has been proposed by defining a partial high order fuzzy time series forecasting model in which the selection of fuzzy lagged variables is done by using genetic algorithms. The proposed method is applied to some real life time series and obtained results are compared with those obtained from other methods available in the literature. It is shown that the proposed method has high forecasting accuracy.

KW - Forecasting

KW - Fuzzy time series

KW - Genetic algorithms

KW - Partial high order model

KW - Variable selection

U2 - 10.1016/j.asoc.2014.03.028

DO - 10.1016/j.asoc.2014.03.028

M3 - Journal article

AN - SCOPUS:84903745219

VL - 22

SP - 465

EP - 473

JO - Applied Soft Computing Journal

JF - Applied Soft Computing Journal

SN - 1568-4946

ER -