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A modified genetic algorithm for forecasting fuzzy time series

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A modified genetic algorithm for forecasting fuzzy time series. / Bas, Eren; Uslu, Vedide Rezan; Yolcu, Ufuk et al.
In: Applied Intelligence, Vol. 41, No. 2, 01.09.2014, p. 453-463.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Bas, E, Uslu, VR, Yolcu, U & Egrioglu, E 2014, 'A modified genetic algorithm for forecasting fuzzy time series', Applied Intelligence, vol. 41, no. 2, pp. 453-463. https://doi.org/10.1007/s10489-014-0529-x

APA

Bas, E., Uslu, V. R., Yolcu, U., & Egrioglu, E. (2014). A modified genetic algorithm for forecasting fuzzy time series. Applied Intelligence, 41(2), 453-463. https://doi.org/10.1007/s10489-014-0529-x

Vancouver

Bas E, Uslu VR, Yolcu U, Egrioglu E. A modified genetic algorithm for forecasting fuzzy time series. Applied Intelligence. 2014 Sept 1;41(2):453-463. doi: 10.1007/s10489-014-0529-x

Author

Bas, Eren ; Uslu, Vedide Rezan ; Yolcu, Ufuk et al. / A modified genetic algorithm for forecasting fuzzy time series. In: Applied Intelligence. 2014 ; Vol. 41, No. 2. pp. 453-463.

Bibtex

@article{f451e399a3404b38a7612b91c3493226,
title = "A modified genetic algorithm for forecasting fuzzy time series",
abstract = "Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.",
keywords = "Forecasting, Fuzzy time series, Genetic algorithm, Mutation operator",
author = "Eren Bas and Uslu, {Vedide Rezan} and Ufuk Yolcu and Erol Egrioglu",
year = "2014",
month = sep,
day = "1",
doi = "10.1007/s10489-014-0529-x",
language = "English",
volume = "41",
pages = "453--463",
journal = "Applied Intelligence",
issn = "0924-669X",
publisher = "Springer Netherlands",
number = "2",

}

RIS

TY - JOUR

T1 - A modified genetic algorithm for forecasting fuzzy time series

AU - Bas, Eren

AU - Uslu, Vedide Rezan

AU - Yolcu, Ufuk

AU - Egrioglu, Erol

PY - 2014/9/1

Y1 - 2014/9/1

N2 - Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.

AB - Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.

KW - Forecasting

KW - Fuzzy time series

KW - Genetic algorithm

KW - Mutation operator

U2 - 10.1007/s10489-014-0529-x

DO - 10.1007/s10489-014-0529-x

M3 - Journal article

AN - SCOPUS:84906787384

VL - 41

SP - 453

EP - 463

JO - Applied Intelligence

JF - Applied Intelligence

SN - 0924-669X

IS - 2

ER -