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 ...
[view complete introduction]

US $
15

*(Excluding Mailing and Handling)



A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process

Pp. 93-110 (18)

Ozge Cagcag Yolcu

Abstract

In the literature, two basic approaches are mentioned for time series forecasting. These are probabilistic and non-probabilistic approaches. This study is focused on fuzzy time series method one of the non-probabilistic approaches. Fuzzy time series analysis methods are the effective methods which are more favourable than traditional methods. The basic stages as fuzzification, identification of fuzzy relations and defuzzification which constitute the fuzzy time series analyses has been affectively used to get a better prediction performance. All of these three stages that are considered separately in analysis process lead to different errors. This situation, therefore, may cause a rise in model error. In order to eliminate this problem in this study all steps can be evaluated in one process synchronously. In the proposed approach, the method similar to fuzzy C-means, multiplicative artificial neural networks and genetic algorithm are used simultaneously in fuzzification, identification of fuzzy relation and determination of all parameters, respectively. And also different fuzzy time series are analysed. All obtained results are discussed to be able to consider the proposed method in terms of forecasting performance.

Keywords:

Fuzzy Time Series, Forecasting, Genetic algorithm, Single multiplicative neuron model.

Affiliation:

Department of Industrial Engineering, Faculty of Engineering, Giresun University, Giresun, Turkey.