Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A high order seasonal fuzzy time series model and application to international tourism demand of Turkey
AU - Aladag, Cagdas Hakan
AU - Egrioglu, Erol
AU - Yolcu, Ufufk
AU - Uslu, Vedide R.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - There have been many recently proposed methods for forecasting fuzzy time series. Most of them are, however, for non-seasonal fuzzy time series. A definition of seasonal fuzzy time series was firstly given by Song (Q. Song, Seasonal forecasting in fuzzy time series, Fuzzy Sets and Systems 107 (1999), 235-236). In his paper, the model was a first order seasonal fuzzy time series. However, real time series behave very rarely in a first order seasonal fuzzy time series structure. There is a need for modeling high order seasonal structures because their structure generally is more complicated. We make a definition for a high order seasonal fuzzy time series and propose a new approach based on artificial neural networks for forecasting a high order seasonal fuzzy time series. This proposed method is applied to the time series of the international tourism demand of Turkey. The results from this approach are compared to the results obtained from conventional seasonal fuzzy time series methods. From this comparisons we observe that the new method improve the forecasting accuracy.
AB - There have been many recently proposed methods for forecasting fuzzy time series. Most of them are, however, for non-seasonal fuzzy time series. A definition of seasonal fuzzy time series was firstly given by Song (Q. Song, Seasonal forecasting in fuzzy time series, Fuzzy Sets and Systems 107 (1999), 235-236). In his paper, the model was a first order seasonal fuzzy time series. However, real time series behave very rarely in a first order seasonal fuzzy time series structure. There is a need for modeling high order seasonal structures because their structure generally is more complicated. We make a definition for a high order seasonal fuzzy time series and propose a new approach based on artificial neural networks for forecasting a high order seasonal fuzzy time series. This proposed method is applied to the time series of the international tourism demand of Turkey. The results from this approach are compared to the results obtained from conventional seasonal fuzzy time series methods. From this comparisons we observe that the new method improve the forecasting accuracy.
KW - Feed forward neural networks
KW - Forecasting
KW - High order fuzzy time series
KW - International tourism demand of Turkey
KW - Seasonal fuzzy time series
U2 - 10.3233/IFS-120738
DO - 10.3233/IFS-120738
M3 - Journal article
AN - SCOPUS:84890735994
VL - 26
SP - 295
EP - 302
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
SN - 1064-1246
IS - 1
ER -