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Type-1 fuzzy time series function method based on binary particle swarm optimisation

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Type-1 fuzzy time series function method based on binary particle swarm optimisation. / Aladag, Cagdas Hakan; Yolcu, Ufuk; Egrioglu, Erol et al.
In: International Journal of Data Analysis Techniques and Strategies, Vol. 8, No. 1, 31.01.2016, p. 2-13.

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

Harvard

Aladag, CH, Yolcu, U, Egrioglu, E & Turksen, IB 2016, 'Type-1 fuzzy time series function method based on binary particle swarm optimisation', International Journal of Data Analysis Techniques and Strategies, vol. 8, no. 1, pp. 2-13. https://doi.org/10.1504/IJDATS.2016.075970

APA

Aladag, C. H., Yolcu, U., Egrioglu, E., & Turksen, I. B. (2016). Type-1 fuzzy time series function method based on binary particle swarm optimisation. International Journal of Data Analysis Techniques and Strategies, 8(1), 2-13. https://doi.org/10.1504/IJDATS.2016.075970

Vancouver

Aladag CH, Yolcu U, Egrioglu E, Turksen IB. Type-1 fuzzy time series function method based on binary particle swarm optimisation. International Journal of Data Analysis Techniques and Strategies. 2016 Jan 31;8(1):2-13. doi: 10.1504/IJDATS.2016.075970

Author

Aladag, Cagdas Hakan ; Yolcu, Ufuk ; Egrioglu, Erol et al. / Type-1 fuzzy time series function method based on binary particle swarm optimisation. In: International Journal of Data Analysis Techniques and Strategies. 2016 ; Vol. 8, No. 1. pp. 2-13.

Bibtex

@article{7e22bf9736cc46a6905ecd7d95990293,
title = "Type-1 fuzzy time series function method based on binary particle swarm optimisation",
abstract = "For time series forecasting four kinds of fuzzy-based approaches can be used. These are fuzzy regression techniques, fuzzy time series methods, fuzzy inference systems, and fuzzy function approaches. There are some major problems in using fuzzy regression techniques and fuzzy inference systems for time series forecasting. Therefore, it would be wise to use a forecasting approach which combines fuzzy time series and fuzzy function approaches. In this study, a fuzzy time series forecasting method based on fuzzy function approach is proposed by adopting fuzzy function approach to time series forecasting. And, the proposed approach is called type-1 fuzzy time series function approach. Also, in the proposed approach, the lagged variables of the system are determined by using binary particle swarm optimisation. In order to evaluate the performance of the proposed method, it has been applied to well-known time series of Australian beer consumption and Istanbul stock exchange dataset.",
keywords = "Forecasting, Fuzzy functions, Fuzzy time series, Fuzzy time series function, Particle swarm optimisation",
author = "Aladag, {Cagdas Hakan} and Ufuk Yolcu and Erol Egrioglu and Turksen, {I. Burhan}",
year = "2016",
month = jan,
day = "31",
doi = "10.1504/IJDATS.2016.075970",
language = "English",
volume = "8",
pages = "2--13",
journal = "International Journal of Data Analysis Techniques and Strategies",
issn = "1755-8050",
publisher = "Inderscience Publishers",
number = "1",

}

RIS

TY - JOUR

T1 - Type-1 fuzzy time series function method based on binary particle swarm optimisation

AU - Aladag, Cagdas Hakan

AU - Yolcu, Ufuk

AU - Egrioglu, Erol

AU - Turksen, I. Burhan

PY - 2016/1/31

Y1 - 2016/1/31

N2 - For time series forecasting four kinds of fuzzy-based approaches can be used. These are fuzzy regression techniques, fuzzy time series methods, fuzzy inference systems, and fuzzy function approaches. There are some major problems in using fuzzy regression techniques and fuzzy inference systems for time series forecasting. Therefore, it would be wise to use a forecasting approach which combines fuzzy time series and fuzzy function approaches. In this study, a fuzzy time series forecasting method based on fuzzy function approach is proposed by adopting fuzzy function approach to time series forecasting. And, the proposed approach is called type-1 fuzzy time series function approach. Also, in the proposed approach, the lagged variables of the system are determined by using binary particle swarm optimisation. In order to evaluate the performance of the proposed method, it has been applied to well-known time series of Australian beer consumption and Istanbul stock exchange dataset.

AB - For time series forecasting four kinds of fuzzy-based approaches can be used. These are fuzzy regression techniques, fuzzy time series methods, fuzzy inference systems, and fuzzy function approaches. There are some major problems in using fuzzy regression techniques and fuzzy inference systems for time series forecasting. Therefore, it would be wise to use a forecasting approach which combines fuzzy time series and fuzzy function approaches. In this study, a fuzzy time series forecasting method based on fuzzy function approach is proposed by adopting fuzzy function approach to time series forecasting. And, the proposed approach is called type-1 fuzzy time series function approach. Also, in the proposed approach, the lagged variables of the system are determined by using binary particle swarm optimisation. In order to evaluate the performance of the proposed method, it has been applied to well-known time series of Australian beer consumption and Istanbul stock exchange dataset.

KW - Forecasting

KW - Fuzzy functions

KW - Fuzzy time series

KW - Fuzzy time series function

KW - Particle swarm optimisation

U2 - 10.1504/IJDATS.2016.075970

DO - 10.1504/IJDATS.2016.075970

M3 - Journal article

AN - SCOPUS:84978394372

VL - 8

SP - 2

EP - 13

JO - International Journal of Data Analysis Techniques and Strategies

JF - International Journal of Data Analysis Techniques and Strategies

SN - 1755-8050

IS - 1

ER -