Joint Propagation of Ontological and Epistemic Uncertainty across Risk Assessment and Fuzzy Time Series Models

Vasile Georgescu1

  1. Department of Statistics and Informatics, University of Craiova, 13 A.I.Cuza
    Craiova, 200375, Romania
    v_geo@yahoo.com

Abstract

This paper discusses hybrid probabilistic and fuzzy set approaches to propagating randomness and imprecision in risk assessment and fuzzy time series models. Stochastic and Computational Intelligence methods, such as Probability bounds analysis, Fuzzy -levels analysis, Fuzzy random vectors, Wavelets decomposition and Wavelets Networks are combined to capture different kinds of uncertainty. Their most appropriate applications are probabilistic risk assessments carried out in terms of probability distributions with imprecise parameters and stochastic processes modeled in terms of fuzzy time series.

Key words

risk assessment, fuzzy time series, probability bounds analysis, fuzzy random vectors, wavelets, Hukuhara difference

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS121215048G

Publication information

Volume 11, Issue 2 (June 2014)
Year of Publication: 2014
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Georgescu, V.: Joint Propagation of Ontological and Epistemic Uncertainty across Risk Assessment and Fuzzy Time Series Models. Computer Science and Information Systems, Vol. 11, No. 2, 881–904. (2014)