ARMA(p,q) type high order fuzzy time series forecast method based on fuzzy logic relations
[ X ]
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Within classic time series approaches, a time series model can be studied under 3 groups, namely AR (autoregressive model), MA (moving averages model) and ARMA (autoregressive moving averages model). On the other hand, solutions are based mostly on fuzzy AR time series models in the fuzzy time series literature. However, just a few fuzzy ARMA time series models have proposed until now. Fuzzy AR time series models have been divided into two groups named first order and high order models in the literature, highlighting the impact of model degree on forecast performance. However, model structure has been disregarded in these fuzzy AR models. Therefore, it is necessary to eliminate the model specification error arising from not utilizing of MA variables in the fuzzy time series approaches. For this reason, a new high order fuzzy ARMA(p,q) time series solution algorithm based on fuzzy logic group relations including fuzzy MA variables along with fuzzy AR variables has been proposed in this study. The main purpose of this article is to show that the forecast performance can be significantly improved when the deficiency of not utilizing MA variables. The other aim is also to show that the proposed method is better than the other fuzzy ARMA time series models in the literature from the point of forecast performance. Therefore, the new proposed method has been compared regarding forecast performance against some methods commonly used in literature by applying them on gold prices in Turkey, Istanbul Stock Exchange (IMKB) and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). © 2017 Elsevier B.V.
Açıklama
Anahtar Kelimeler
Fuzzy ARMA Models, Fuzzy Autoregressive – Moving Avarage Model, Fuzzy Time Series, Group Relation Table, High Order Fuzzy Time Series
Kaynak
Applied Soft Computing Journal
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
58
Sayı
Künye
Koçak, C. (2017). ARMA (p, q) type high order fuzzy time series forecast method based on fuzzy logic relations. Applied Soft Computing, 58, 92-103.
