In this paper, we deal with several time series of share prices and daily returns of different companies which are members of the Tehran Stock
Exchange. Three prediction methods are used for time series forecasting. The
first method, is based on the linear models (ARIMA) for short term and long term forecasting. The second method, is based on the nonlinear neural
networks model and the third method is a neural networks model with a
special structure. It has been shown the time series generator process of these
companies are complex nonlinear mappings and the methods based on the
various linear modelling strategies
are unable to identify these dynamics.
Also, it has been shown by using the conventional structure of the nonlinear neural networks that one can not obtain a satisfactory results for long term
forecasting. Finally, it is shown that the proposed structrure, provides accurate next step and the long term share prices and daily
returns forecasting.