A New High Order Multivariate Fuzzy Time Series Forecasting Model
Pp. 127-143 (17)
In many disciplines, including uncertainty of data obtained from time series
problems generates the needs to use fuzzy time series methods which do not need to
check some strict assumptions of conventional time series methods. Although, there are
many other well-known prediction methods in the fuzzy time series literature, most of
them comprise of univariate methods and these methods may fail to satisfy to analysis
of the data which contain multivariate relationships. In this study, the new multivariate
fuzzy time series approach is proposed. The proposed approach uses fuzzy C-means
method to determine the membership values in the fuzzification stage, and also this
new multivariate approach makes use of single multiplicative neuron model artificial
neural network for the identification of the multivariate fuzzy relations. In the
identification of fuzzy relations stage, membership values are used to avoid the
information loss. The proposed methods’ performance has been assessed by applying it
to different data sets.
Artificial neural network, Forecasting, Fuzzy c-means, Membership
degree, Multivariate fuzzy time series, Single multiplicative neuron model.
Department of Econometrics, Faculty of Economics and Administrative Sciences, Giresun University, Giresun, Turkey.