A new approach based on artificial neural networks for high order bivariate fuzzy time series

Associated organisational unit

Centre for Marketing Analytics

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

A new approach based on artificial neural networks for high order bivariate fuzzy time series. / Egrioglu, Erol; Uslu, V. Rezan; Yolcu, Ufuk et al.
Applications of Soft Computing: From Theory to Praxis. Springer-Verlag, 2009. p. 265-273 (Advances in Intelligent and Soft Computing; Vol. 58).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Egrioglu, E, Uslu, VR, Yolcu, U, Basaran, MA & Hakan, AC 2009, A new approach based on artificial neural networks for high order bivariate fuzzy time series. in Applications of Soft Computing: From Theory to Praxis. Advances in Intelligent and Soft Computing, vol. 58, Springer-Verlag, pp. 265-273.

APA

Egrioglu, E., Uslu, V. R., Yolcu, U., Basaran, M. A., & Hakan, A. C. (2009). A new approach based on artificial neural networks for high order bivariate fuzzy time series. In Applications of Soft Computing: From Theory to Praxis (pp. 265-273). (Advances in Intelligent and Soft Computing; Vol. 58). Springer-Verlag.

Vancouver

Egrioglu E, Uslu VR, Yolcu U, Basaran MA, Hakan AC. A new approach based on artificial neural networks for high order bivariate fuzzy time series. In Applications of Soft Computing: From Theory to Praxis. Springer-Verlag. 2009. p. 265-273. (Advances in Intelligent and Soft Computing).

Author

Egrioglu, Erol ; Uslu, V. Rezan ; Yolcu, Ufuk et al. / A new approach based on artificial neural networks for high order bivariate fuzzy time series. Applications of Soft Computing: From Theory to Praxis. Springer-Verlag, 2009. pp. 265-273 (Advances in Intelligent and Soft Computing).

Bibtex

@inproceedings{a98d55062acb4a388219ba7e40648783,

title = "A new approach based on artificial neural networks for high order bivariate fuzzy time series",

abstract = "When observations of time series are defined linguistically or do not follow the assumptions required for time series theory, the classical methods of time series analysis do not cope with fuzzy numbers and assumption violations. Therefore, forecasts are not reliable. [8], [9] gave a definition of fuzzy time series which have fuzzy observations and proposed a forecast method for it. In recent years, many researches about univariate fuzzy time series have been conducted. In [6], [5], [7], [4] and [10] bivariate fuzzy time series approaches have been proposed. In this study, a new method for high order bivariate fuzzy time series in which fuzzy relationships are determined by artificial neural networks (ANN) is proposed and the real data application of the proposed method is presented.",

author = "Erol Egrioglu and Uslu, {V. Rezan} and Ufuk Yolcu and Basaran, {M. A.} and Hakan, {Aladag C.}",

year = "2009",

month = jan,

day = "1",

language = "English",

isbn = "9783540896180",

series = "Advances in Intelligent and Soft Computing",

publisher = "Springer-Verlag",

pages = "265--273",

booktitle = "Applications of Soft Computing",

}

RIS

TY - GEN

T1 - A new approach based on artificial neural networks for high order bivariate fuzzy time series

AU - Egrioglu, Erol

AU - Uslu, V. Rezan

AU - Yolcu, Ufuk

AU - Basaran, M. A.

AU - Hakan, Aladag C.

PY - 2009/1/1

Y1 - 2009/1/1

N2 - When observations of time series are defined linguistically or do not follow the assumptions required for time series theory, the classical methods of time series analysis do not cope with fuzzy numbers and assumption violations. Therefore, forecasts are not reliable. [8], [9] gave a definition of fuzzy time series which have fuzzy observations and proposed a forecast method for it. In recent years, many researches about univariate fuzzy time series have been conducted. In [6], [5], [7], [4] and [10] bivariate fuzzy time series approaches have been proposed. In this study, a new method for high order bivariate fuzzy time series in which fuzzy relationships are determined by artificial neural networks (ANN) is proposed and the real data application of the proposed method is presented.

AB - When observations of time series are defined linguistically or do not follow the assumptions required for time series theory, the classical methods of time series analysis do not cope with fuzzy numbers and assumption violations. Therefore, forecasts are not reliable. [8], [9] gave a definition of fuzzy time series which have fuzzy observations and proposed a forecast method for it. In recent years, many researches about univariate fuzzy time series have been conducted. In [6], [5], [7], [4] and [10] bivariate fuzzy time series approaches have been proposed. In this study, a new method for high order bivariate fuzzy time series in which fuzzy relationships are determined by artificial neural networks (ANN) is proposed and the real data application of the proposed method is presented.

M3 - Conference contribution/Paper

AN - SCOPUS:79551652450

SN - 9783540896180

T3 - Advances in Intelligent and Soft Computing

SP - 265

EP - 273

BT - Applications of Soft Computing

PB - Springer-Verlag

ER -

Research

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