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An enhanced fuzzy time series forecasting method based on artificial bee colony

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

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An enhanced fuzzy time series forecasting method based on artificial bee colony. / Yolcu, Ufuk; Cagcag, Ozge; Aladag, Cagdas Hakan et al.
In: Journal of Intelligent and Fuzzy Systems, Vol. 26, No. 6, 01.01.2014, p. 2627-2637.

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

Harvard

Yolcu, U, Cagcag, O, Aladag, CH & Egrioglu, E 2014, 'An enhanced fuzzy time series forecasting method based on artificial bee colony', Journal of Intelligent and Fuzzy Systems, vol. 26, no. 6, pp. 2627-2637. https://doi.org/10.3233/IFS-130933

APA

Yolcu, U., Cagcag, O., Aladag, C. H., & Egrioglu, E. (2014). An enhanced fuzzy time series forecasting method based on artificial bee colony. Journal of Intelligent and Fuzzy Systems, 26(6), 2627-2637. https://doi.org/10.3233/IFS-130933

Vancouver

Yolcu U, Cagcag O, Aladag CH, Egrioglu E. An enhanced fuzzy time series forecasting method based on artificial bee colony. Journal of Intelligent and Fuzzy Systems. 2014 Jan 1;26(6):2627-2637. doi: 10.3233/IFS-130933

Author

Yolcu, Ufuk ; Cagcag, Ozge ; Aladag, Cagdas Hakan et al. / An enhanced fuzzy time series forecasting method based on artificial bee colony. In: Journal of Intelligent and Fuzzy Systems. 2014 ; Vol. 26, No. 6. pp. 2627-2637.

Bibtex

@article{46a46dc98b11456ead09bba17f5bc005,
title = "An enhanced fuzzy time series forecasting method based on artificial bee colony",
abstract = "In recent years, several forecasting methods have been proposed for the analysis of fuzzy time series. Determination of fuzzy relations and establishing interval lengths, which is used in partition of universe of discourse, can be considered as the two of main elements affecting the forecasting performance of these forecasting methods. In the literature, along with the studies in which interval lengths are determined subjectively, algorithms such as genetic algorithms and particle swarm optimization have been utilized. In this study, a new fuzzy time series forecasting method which uses Artificial Bee Colony (ABC) algorithm for the determination of interval lengths for the first time in the literature is proposed. To obtain forecasts, this new method makes use of fuzzy logic relationship tables in determining the fuzzy relations and also uses estimating based on next state (EBN) for training set and master voting (MV) scheme for test set. The new proposed method is applied to three various time series and when compared with the existing methods better results are obtained with regard to both training and test set..",
keywords = "Artificial bee colony, forecasting, fuzzification, fuzzy time series",
author = "Ufuk Yolcu and Ozge Cagcag and Aladag, {Cagdas Hakan} and Erol Egrioglu",
year = "2014",
month = jan,
day = "1",
doi = "10.3233/IFS-130933",
language = "English",
volume = "26",
pages = "2627--2637",
journal = "Journal of Intelligent and Fuzzy Systems",
issn = "1064-1246",
publisher = "IOS Press",
number = "6",

}

RIS

TY - JOUR

T1 - An enhanced fuzzy time series forecasting method based on artificial bee colony

AU - Yolcu, Ufuk

AU - Cagcag, Ozge

AU - Aladag, Cagdas Hakan

AU - Egrioglu, Erol

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In recent years, several forecasting methods have been proposed for the analysis of fuzzy time series. Determination of fuzzy relations and establishing interval lengths, which is used in partition of universe of discourse, can be considered as the two of main elements affecting the forecasting performance of these forecasting methods. In the literature, along with the studies in which interval lengths are determined subjectively, algorithms such as genetic algorithms and particle swarm optimization have been utilized. In this study, a new fuzzy time series forecasting method which uses Artificial Bee Colony (ABC) algorithm for the determination of interval lengths for the first time in the literature is proposed. To obtain forecasts, this new method makes use of fuzzy logic relationship tables in determining the fuzzy relations and also uses estimating based on next state (EBN) for training set and master voting (MV) scheme for test set. The new proposed method is applied to three various time series and when compared with the existing methods better results are obtained with regard to both training and test set..

AB - In recent years, several forecasting methods have been proposed for the analysis of fuzzy time series. Determination of fuzzy relations and establishing interval lengths, which is used in partition of universe of discourse, can be considered as the two of main elements affecting the forecasting performance of these forecasting methods. In the literature, along with the studies in which interval lengths are determined subjectively, algorithms such as genetic algorithms and particle swarm optimization have been utilized. In this study, a new fuzzy time series forecasting method which uses Artificial Bee Colony (ABC) algorithm for the determination of interval lengths for the first time in the literature is proposed. To obtain forecasts, this new method makes use of fuzzy logic relationship tables in determining the fuzzy relations and also uses estimating based on next state (EBN) for training set and master voting (MV) scheme for test set. The new proposed method is applied to three various time series and when compared with the existing methods better results are obtained with regard to both training and test set..

KW - Artificial bee colony

KW - forecasting

KW - fuzzification

KW - fuzzy time series

U2 - 10.3233/IFS-130933

DO - 10.3233/IFS-130933

M3 - Journal article

AN - SCOPUS:84901790108

VL - 26

SP - 2627

EP - 2637

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

SN - 1064-1246

IS - 6

ER -