A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process
Pp. 93-110 (18)
Ozge Cagcag Yolcu
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.
Fuzzy Time Series, Forecasting, Genetic algorithm, Single
multiplicative neuron model.
Department of Industrial Engineering, Faculty of Engineering, Giresun University, Giresun, Turkey.