publication . Article . 2009

Statistical and RBF NN models: Providing forecasts and risk assessment

Milan Marček;
Open Access
  • Published: 01 Jan 2009 Journal: Ekonomická revue - Central European Review of Economic Issues, volume 12, pages 175-182 (issn: 1212-3951, Copyright policy)
  • Publisher: Faculty of Economics, VSB Technical University of Ostrava
  • Country: Czech Republic
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.
Persistent Identifiers
free text keywords: Machine learning, computer.software_genre, computer, Granular computing, Risk evaluation, Engineering, business.industry, business, Artificial intelligence, White noise, Data mining, Risk assessment
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