Forecast accuracy of economic and financial processes is a popular measure for quantifying the risk in multi-criteria decision making. In this paper, we develop forecasting models based on the statistical (stochastic) methods, sometimes called as hard computing, and on the soft methods using granular computing. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented and an application is included. We show a new approach of function estimation for nonlinear time series model by means of a granular fuzzy logic neural network based on Gaussian activation function with cloud concept. In a comparative study is shown that the presented approach is able to model and predict the high frequency data with reasonable accuracy and more efficient than the statistical methods.
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