A new time-invariant fuzzy time series forecasting method based on genetic algorithm

Management Science

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https://doi.org/10.1155/2012/785709
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A new time-invariant fuzzy time series forecasting method based on genetic algorithm. / Eǧrioǧlu, Erol.
In: Advances in Fuzzy Systems, Vol. 2012, 785709, 17.08.2012.

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

Bibtex

@article{09d560545c714afda57847351855b4e8,

title = "A new time-invariant fuzzy time series forecasting method based on genetic algorithm",

abstract = "In recent years, many fuzzy time series methods have been proposed in the literature. Some of these methods use the classical fuzzy set theory, which needs complex matricial operations in fuzzy time series methods. Because of this problem, many studies in the literature use fuzzy group relationship tables. Since the fuzzy relationship tables use order of fuzzy sets, the membership functions of fuzzy sets have not been taken into consideration. In this study, a new method that employs membership functions of fuzzy sets is proposed. The new method determines elements of fuzzy relation matrix based on genetic algorithms. The proposed method uses first-order fuzzy time series forecasting model, and it is applied to the several data sets. As a result of implementation, it is obtained that the proposed method outperforms some methods in the literature.",

author = "Erol Eǧrioǧlu",

year = "2012",

month = aug,

day = "17",

doi = "10.1155/2012/785709",

language = "English",

volume = "2012",

journal = "Advances in Fuzzy Systems",

issn = "1687-7101",

publisher = "Hindawi",

}

RIS

TY - JOUR

T1 - A new time-invariant fuzzy time series forecasting method based on genetic algorithm

AU - Eǧrioǧlu, Erol

PY - 2012/8/17

Y1 - 2012/8/17

N2 - In recent years, many fuzzy time series methods have been proposed in the literature. Some of these methods use the classical fuzzy set theory, which needs complex matricial operations in fuzzy time series methods. Because of this problem, many studies in the literature use fuzzy group relationship tables. Since the fuzzy relationship tables use order of fuzzy sets, the membership functions of fuzzy sets have not been taken into consideration. In this study, a new method that employs membership functions of fuzzy sets is proposed. The new method determines elements of fuzzy relation matrix based on genetic algorithms. The proposed method uses first-order fuzzy time series forecasting model, and it is applied to the several data sets. As a result of implementation, it is obtained that the proposed method outperforms some methods in the literature.

AB - In recent years, many fuzzy time series methods have been proposed in the literature. Some of these methods use the classical fuzzy set theory, which needs complex matricial operations in fuzzy time series methods. Because of this problem, many studies in the literature use fuzzy group relationship tables. Since the fuzzy relationship tables use order of fuzzy sets, the membership functions of fuzzy sets have not been taken into consideration. In this study, a new method that employs membership functions of fuzzy sets is proposed. The new method determines elements of fuzzy relation matrix based on genetic algorithms. The proposed method uses first-order fuzzy time series forecasting model, and it is applied to the several data sets. As a result of implementation, it is obtained that the proposed method outperforms some methods in the literature.

U2 - 10.1155/2012/785709

DO - 10.1155/2012/785709

M3 - Journal article

AN - SCOPUS:84864952412

VL - 2012

JO - Advances in Fuzzy Systems

JF - Advances in Fuzzy Systems

SN - 1687-7101

M1 - 785709

ER -

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