A New Approach Based on Artificial Neural Networks for High Order Bivariate Fuzzy Time Series
Küçük Resim Yok
Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
When observations of time series are defined linguistically or do not follow the assumptions required for time series theory, the classical methods of time series analysis do not cope with fuzzy numbers and assumption violations. Therefore, forecasts are not reliable. [8], [9] gave a definition of fuzzy time series which have fuzzy observations and proposed a forecast method for it. In recent years, many researches about univariate fuzzy time series have been conducted. In [6], [5], [7], [4] and [10] bivariate fuzzy time series approaches have been proposed. In this study, a new method for high order bivariate fuzzy time series in which fuzzy relationships are determined by artificial neural networks (ANN) is proposed and the real data application of the proposed method is presented.
Açıklama
13th World Conference on Soft Computing in Industrial Application -- 2008 -- ELECTR NETWORK
Anahtar Kelimeler
Kaynak
APPLICATIONS OF SOFT COMPUTING: FROM THEORY TO PRAXIS
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
58
