Advances in Time Series Forecasting

Advances in Time Series Forecasting

Volume: 2

This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting ...
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A New High Order Multivariate Fuzzy Time Series Forecasting Model

Pp. 127-143 (17)

Ufuk Yolcu

Abstract

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.

Keywords:

Artificial neural network, Forecasting, Fuzzy c-means, Membership degree, Multivariate fuzzy time series, Single multiplicative neuron model.

Affiliation:

Department of Econometrics, Faculty of Economics and Administrative Sciences, Giresun University, Giresun, Turkey.