Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A novel seasonal fuzzy time series method
AU - Alpaslan, Faruk
AU - Cagcag, Ozge
AU - Aladag, C. H.
AU - Yolcu, U.
AU - Egrioglu, E.
PY - 2012/12/4
Y1 - 2012/12/4
N2 - Fuzzy time series forecasting methods, which have been widely studied in recent years, do not require constraints as found in conventional approaches. On the other hand, most of the time series encountered in real life should be considered as fuzzy time series due to the vagueness that they contain. Although numerous methods have been proposed for the analysis of time series in the literature, these methods fail to forecast seasonal fuzzy time series. The limited number of seasonal fuzzy time series methods consider only the fuzzy set having the highest membership value, rather than the membership value of observations belonging to each fuzzy set. This is contrary to fuzzy set theory and causes information loss, thus affecting forecasting performance negatively. In this study, a new seasonal fuzzy time series method which considers the membership value of the observations belonging to each set in both forecasting fuzzy relations and in the defuzzification step is proposed. The proposed method is applied to a real seasonal time series.
AB - Fuzzy time series forecasting methods, which have been widely studied in recent years, do not require constraints as found in conventional approaches. On the other hand, most of the time series encountered in real life should be considered as fuzzy time series due to the vagueness that they contain. Although numerous methods have been proposed for the analysis of time series in the literature, these methods fail to forecast seasonal fuzzy time series. The limited number of seasonal fuzzy time series methods consider only the fuzzy set having the highest membership value, rather than the membership value of observations belonging to each fuzzy set. This is contrary to fuzzy set theory and causes information loss, thus affecting forecasting performance negatively. In this study, a new seasonal fuzzy time series method which considers the membership value of the observations belonging to each set in both forecasting fuzzy relations and in the defuzzification step is proposed. The proposed method is applied to a real seasonal time series.
KW - Feed forward artificial neural network
KW - Fuzzy c-means
KW - Fuzzy time series
KW - SARIMA
M3 - Journal article
AN - SCOPUS:84870273208
VL - 41
SP - 375
EP - 385
JO - Hacettepe Journal of Mathematics and Statistics
JF - Hacettepe Journal of Mathematics and Statistics
SN - 1303-5010
IS - 3
ER -