A New Fuzzy Time Series Forecasting Model with Neural Network Structure
Pp. 24-36 (13)
Eren Bas and Erol Egrioglu
Non-probabilistic forecasting methods are one of the most popular
forecasting methods in recent years. Fuzzy time series methods are non-probabilistic
and non-linear methods. Although these methods have superior forecasting
performance, linear autoregressive models have better forecasting performance than
fuzzy time series methods for some real-life time series. In this paper, a new hybrid
forecasting method that contains stochastic approach based on an autoregressive model
and fuzzy time series forecasting model was proposed in a network structure. Fuzzy c
means method is used in fuzzification stage of the proposed method and also the
proposed method is trained by using particle swarm optimization. The proposed
method is applied to a well-known real-life time series data and it is proved that the
proposed method has best forecasting performance when compared with some other
studies suggested in the literature.
Autoregressive model, Forecasting, Fuzzy c-means, Fuzzy time
series, Non-linear time series, Particle swarm optimization.
Department of Statistics, Faculty of Arts and Sciences, Giresun University, Giresun, Turkey.