Artificial neural networks for forecasting of fuzzy time series

In this article, an artificial neural network for modeling and forecasting of fuzzy time series is presented. Modeling fuzzy time series with fuzzy data as random realizations of an underlying fuzzy random process enables forecasting of future fuzzy data following the observed time series. Analysis and forecasting of time series with fuzzy data may be carried out with the aid of artificial neural networks. A significant advantage is the fact that neural networks do not require a predetermined process model to simulate and forecast time series possessing fuzzy random characteristics. Artificial neural networks have the ability to learn the characteristics of an existing fuzzy time series, to represent the underlying fuzzy random process, and to forecast future fuzzy data following the time series observed. The algorithms developed are demonstrated using a numerical example.