A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

Egrioglu, Erol and Aladag, Cagdas Hakan and Yolcu, Ufuk and Basaran, Murat A. and Uslu, Vedide R. (2009) A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. Expert Systems with Applications, 36 (4). pp. 7424-7434. ISSN 0957-4174

Full text not available from this repository.

Abstract

In the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time series is obtained by using the previous observation. In other words, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for some time series such as seasonal time series which is an important class in time series models. Besides, the time series encountered in real life have not only autoregressive (AR) structure but also moving average (MA) structure. The fuzzy time series models available in the literature are AR structured and are not appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy time series forecasting model which is first introduced in this paper. The order of this model is determined by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method, real time series are analyzed with this method. The results obtained from the proposed method are compared with the other methods. As a result, it is observed that more accurate results are obtained from the proposed hybrid method.

Item Type: Journal Article
Journal or Publication Title: Expert Systems with Applications
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1702
Subjects:
Departments: Lancaster University Management School > Management Science
Faculty of Science and Technology > Mathematics and Statistics
ID Code: 139567
Deposited By: ep_importer_pure
Deposited On: 13 Dec 2019 15:30
Refereed?: Yes
Published?: Published
Last Modified: 11 Feb 2020 05:45
URI: https://eprints.lancs.ac.uk/id/eprint/139567

Actions (login required)

View Item View Item