Home > Research > Publications & Outputs > A new seasonal fuzzy time series method based o...
View graph of relations

A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA. / Aladag, Sibel; Aladag, Cagdas Hakan; Mentes, Turhan; Egrioglu, Erol.

In: Hacettepe Journal of Mathematics and Statistics, Vol. 41, No. 3, 04.12.2012, p. 337-345.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Aladag, S, Aladag, CH, Mentes, T & Egrioglu, E 2012, 'A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA', Hacettepe Journal of Mathematics and Statistics, vol. 41, no. 3, pp. 337-345.

APA

Aladag, S., Aladag, C. H., Mentes, T., & Egrioglu, E. (2012). A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA. Hacettepe Journal of Mathematics and Statistics, 41(3), 337-345.

Vancouver

Aladag S, Aladag CH, Mentes T, Egrioglu E. A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA. Hacettepe Journal of Mathematics and Statistics. 2012 Dec 4;41(3):337-345.

Author

Aladag, Sibel ; Aladag, Cagdas Hakan ; Mentes, Turhan ; Egrioglu, Erol. / A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA. In: Hacettepe Journal of Mathematics and Statistics. 2012 ; Vol. 41, No. 3. pp. 337-345.

Bibtex

@article{a66e892781e54a89adad9980fc130c90,
title = "A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA",
abstract = "When fuzzy time series include a seasonal component, conventional fuzzy time series models are not sufficient. For such fuzzy time series, lagged variables which are around the period of the time series should also be included in the model. Determining the lagged variables which will be in the forecasting model is a vital issue. Also, defining fuzzy relations is another important issue in the fuzzy time series approach. When the number of fuzzy lagged variables is large, using artificial neural networks to define fuzzy relations makes the operations easier and increases the forecasting accuracy. In this study, in order to deal with the problem of determining the lagged variables, and defining the fuzzy relations, a novel seasonal fuzzy time series approach based on SARIMA and the multiplicative neuron model is proposed. In the proposed method, the SARIMA method is exploited to choose the fuzzy lagged variables and multiplicative neuron model is employed to establish the fuzzy relations. To show the applicability of the proposed method, it is applied to the invoice sum accrued to health service providers. For comparison, the data is also analyzed with other fuzzy time series approaches in the literature. It is observed that the proposed method has the best forecasting accuracy with respect to other methods.",
keywords = "Forecasting, Fuzzy time series, Multiplicative neuron model, SARIMA",
author = "Sibel Aladag and Aladag, {Cagdas Hakan} and Turhan Mentes and Erol Egrioglu",
year = "2012",
month = dec,
day = "4",
language = "English",
volume = "41",
pages = "337--345",
journal = "Hacettepe Journal of Mathematics and Statistics",
issn = "1303-5010",
publisher = "Hacettepe University",
number = "3",

}

RIS

TY - JOUR

T1 - A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA

AU - Aladag, Sibel

AU - Aladag, Cagdas Hakan

AU - Mentes, Turhan

AU - Egrioglu, Erol

PY - 2012/12/4

Y1 - 2012/12/4

N2 - When fuzzy time series include a seasonal component, conventional fuzzy time series models are not sufficient. For such fuzzy time series, lagged variables which are around the period of the time series should also be included in the model. Determining the lagged variables which will be in the forecasting model is a vital issue. Also, defining fuzzy relations is another important issue in the fuzzy time series approach. When the number of fuzzy lagged variables is large, using artificial neural networks to define fuzzy relations makes the operations easier and increases the forecasting accuracy. In this study, in order to deal with the problem of determining the lagged variables, and defining the fuzzy relations, a novel seasonal fuzzy time series approach based on SARIMA and the multiplicative neuron model is proposed. In the proposed method, the SARIMA method is exploited to choose the fuzzy lagged variables and multiplicative neuron model is employed to establish the fuzzy relations. To show the applicability of the proposed method, it is applied to the invoice sum accrued to health service providers. For comparison, the data is also analyzed with other fuzzy time series approaches in the literature. It is observed that the proposed method has the best forecasting accuracy with respect to other methods.

AB - When fuzzy time series include a seasonal component, conventional fuzzy time series models are not sufficient. For such fuzzy time series, lagged variables which are around the period of the time series should also be included in the model. Determining the lagged variables which will be in the forecasting model is a vital issue. Also, defining fuzzy relations is another important issue in the fuzzy time series approach. When the number of fuzzy lagged variables is large, using artificial neural networks to define fuzzy relations makes the operations easier and increases the forecasting accuracy. In this study, in order to deal with the problem of determining the lagged variables, and defining the fuzzy relations, a novel seasonal fuzzy time series approach based on SARIMA and the multiplicative neuron model is proposed. In the proposed method, the SARIMA method is exploited to choose the fuzzy lagged variables and multiplicative neuron model is employed to establish the fuzzy relations. To show the applicability of the proposed method, it is applied to the invoice sum accrued to health service providers. For comparison, the data is also analyzed with other fuzzy time series approaches in the literature. It is observed that the proposed method has the best forecasting accuracy with respect to other methods.

KW - Forecasting

KW - Fuzzy time series

KW - Multiplicative neuron model

KW - SARIMA

M3 - Journal article

AN - SCOPUS:84870277987

VL - 41

SP - 337

EP - 345

JO - Hacettepe Journal of Mathematics and Statistics

JF - Hacettepe Journal of Mathematics and Statistics

SN - 1303-5010

IS - 3

ER -