Home > Research > Publications & Outputs > A new hybrid approach based on SARIMA and parti...

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. / Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk et al.
In: Expert Systems with Applications, Vol. 36, No. 4, 01.05.2009, p. 7424-7434.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Egrioglu, E, Aladag, CH, Yolcu, U, Basaran, MA & Uslu, VR 2009, 'A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model', Expert Systems with Applications, vol. 36, no. 4, pp. 7424-7434. https://doi.org/10.1016/j.eswa.2008.09.040

APA

Egrioglu, E., Aladag, C. H., Yolcu, U., Basaran, M. A., & Uslu, V. 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), 7424-7434. https://doi.org/10.1016/j.eswa.2008.09.040

Vancouver

Egrioglu E, Aladag CH, Yolcu U, Basaran MA, Uslu VR. A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. Expert Systems with Applications. 2009 May 1;36(4):7424-7434. doi: 10.1016/j.eswa.2008.09.040

Author

Egrioglu, Erol ; Aladag, Cagdas Hakan ; Yolcu, Ufuk et al. / A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. In: Expert Systems with Applications. 2009 ; Vol. 36, No. 4. pp. 7424-7434.

Bibtex

@article{fab09657542b4c7ebc05b55f29b610f0,
title = "A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model",
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.",
keywords = "Bivariate, Box-Jenkins method, Feed forward neural networks, Forecasting, High order, Seasonal fuzzy time series",
author = "Erol Egrioglu and Aladag, {Cagdas Hakan} and Ufuk Yolcu and Basaran, {Murat A.} and Uslu, {Vedide R.}",
year = "2009",
month = may,
day = "1",
doi = "10.1016/j.eswa.2008.09.040",
language = "English",
volume = "36",
pages = "7424--7434",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",
number = "4",

}

RIS

TY - JOUR

T1 - A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

AU - Egrioglu, Erol

AU - Aladag, Cagdas Hakan

AU - Yolcu, Ufuk

AU - Basaran, Murat A.

AU - Uslu, Vedide R.

PY - 2009/5/1

Y1 - 2009/5/1

N2 - 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.

AB - 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.

KW - Bivariate

KW - Box-Jenkins method

KW - Feed forward neural networks

KW - Forecasting

KW - High order

KW - Seasonal fuzzy time series

U2 - 10.1016/j.eswa.2008.09.040

DO - 10.1016/j.eswa.2008.09.040

M3 - Journal article

AN - SCOPUS:60249083330

VL - 36

SP - 7424

EP - 7434

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 4

ER -