Vysoká škola báňská - Technická univerzita Ostrava
Doi
Abstract
Forecast accuracy of economic and financial processes is a popular measure for quantifying the risk in decision
making. In this paper, we develop forecasting models based on statistical (stochastic) methods, sometimes called
hard computing, and on a soft method using granular computing. We consider the accuracy of forecasting models
as a measure for risk evaluation. It is found that the risk estimation process based on soft methods is simplified
and less critical to the question whether the data is true crisp or white noise
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