Empirical studies show that there is stronger dependency between large losses than large profit in financial market, which undermine the performance of using symmetric distribution for modeling these asymmetric. That is why the assuming normal joint distribution of returns is not suitable because of considering the linier dependence, and can be lead to inappropriate estimate of VaR. Copula theo...

An approach to data-driven linguistic modeling is presented. The methodology is based on a fuzzy system with relational input partition that allows for transparent modeling of linear dependencies between the inputs. An identification algorithm for this type of fuzzy system is proposed. It automatically finds strongest dependencies from numerical data. An application example illustrates the usef...

We explore neural higher-order input-dependent factors in linear-chain conditional random fields (LC-CRFs) for sequence labeling. Higher-order LC-CRFs with linear factors are wellestablished for sequence labeling tasks, but they lack the ability to model non-linear dependencies. These non-linear dependencies, however, can be efficiently modelled by neural higher-order input-dependent factors wh...