Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -