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 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.