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

Egrioglu, Erol and Uslu, V. Rezan and Yolcu, Ufuk and Basaran, M. A. and Hakan, Aladag C. (2009) A new approach based on artificial neural networks for high order bivariate fuzzy time series. In: Applications of Soft Computing. Advances in Intelligent and Soft Computing . Springer-Verlag, pp. 265-273. ISBN 9783540896180

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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.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700
Subjects:
ID Code:
139559
Deposited By:
Deposited On:
13 Dec 2019 15:25
Refereed?:
Yes
Published?:
Published
Last Modified:
20 May 2020 09:00