Home > Research > Publications & Outputs > PSO-based high order time invariant fuzzy time ...

Research

Links

Text available via DOI:

Keywords

View graph of relations

PSO-based high order time invariant fuzzy time series method: Application to stock exchange data

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

Published

Standard

PSO-based high order time invariant fuzzy time series method: Application to stock exchange data. / Egrioglu, Erol.
In: Economic Modelling, Vol. 38, 01.01.2014, p. 633-639.

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

Harvard

Egrioglu, E 2014, 'PSO-based high order time invariant fuzzy time series method: Application to stock exchange data', Economic Modelling, vol. 38, pp. 633-639. https://doi.org/10.1016/j.econmod.2014.02.017

APA

Egrioglu, E. (2014). PSO-based high order time invariant fuzzy time series method: Application to stock exchange data. Economic Modelling, 38, 633-639. https://doi.org/10.1016/j.econmod.2014.02.017

Vancouver

Egrioglu E. PSO-based high order time invariant fuzzy time series method: Application to stock exchange data. Economic Modelling. 2014 Jan 1;38:633-639. doi: 10.1016/j.econmod.2014.02.017

Author

Egrioglu, Erol. / PSO-based high order time invariant fuzzy time series method : Application to stock exchange data. In: Economic Modelling. 2014 ; Vol. 38. pp. 633-639.

Bibtex

@article{b44667656622470aa7e40d39b87dc737,
title = "PSO-based high order time invariant fuzzy time series method: Application to stock exchange data",
abstract = "Fuzzy time series methods are effective techniques to forecast time series. Fuzzy time series methods are based on fuzzy set theory. In the early years, classical fuzzy set operations were used in the fuzzy time series methods. In recent years, artificial intelligence techniques have been used in different stages of fuzzy time series methods. In this paper, a novel fuzzy time series method which is based on particle swarm optimization is proposed. A high order fuzzy time series forecasting model is used in the proposed method. In the proposed method, determination of fuzzy relations is performed by estimating the optimal fuzzy relation matrix. The performance of the proposed method is compared to some methods in the literature by using three real world time series. It is shown that the proposed method has better performance than other methods in the literature.",
keywords = "Define fuzzy relation, Forecasting, Fuzzy c-means, Fuzzy time series, Particle swarm optimization",
author = "Erol Egrioglu",
year = "2014",
month = jan,
day = "1",
doi = "10.1016/j.econmod.2014.02.017",
language = "English",
volume = "38",
pages = "633--639",
journal = "Economic Modelling",
issn = "0264-9993",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - PSO-based high order time invariant fuzzy time series method

T2 - Application to stock exchange data

AU - Egrioglu, Erol

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Fuzzy time series methods are effective techniques to forecast time series. Fuzzy time series methods are based on fuzzy set theory. In the early years, classical fuzzy set operations were used in the fuzzy time series methods. In recent years, artificial intelligence techniques have been used in different stages of fuzzy time series methods. In this paper, a novel fuzzy time series method which is based on particle swarm optimization is proposed. A high order fuzzy time series forecasting model is used in the proposed method. In the proposed method, determination of fuzzy relations is performed by estimating the optimal fuzzy relation matrix. The performance of the proposed method is compared to some methods in the literature by using three real world time series. It is shown that the proposed method has better performance than other methods in the literature.

AB - Fuzzy time series methods are effective techniques to forecast time series. Fuzzy time series methods are based on fuzzy set theory. In the early years, classical fuzzy set operations were used in the fuzzy time series methods. In recent years, artificial intelligence techniques have been used in different stages of fuzzy time series methods. In this paper, a novel fuzzy time series method which is based on particle swarm optimization is proposed. A high order fuzzy time series forecasting model is used in the proposed method. In the proposed method, determination of fuzzy relations is performed by estimating the optimal fuzzy relation matrix. The performance of the proposed method is compared to some methods in the literature by using three real world time series. It is shown that the proposed method has better performance than other methods in the literature.

KW - Define fuzzy relation

KW - Forecasting

KW - Fuzzy c-means

KW - Fuzzy time series

KW - Particle swarm optimization

U2 - 10.1016/j.econmod.2014.02.017

DO - 10.1016/j.econmod.2014.02.017

M3 - Journal article

AN - SCOPUS:84896542209

VL - 38

SP - 633

EP - 639

JO - Economic Modelling

JF - Economic Modelling

SN - 0264-9993

ER -