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In this paper, we propose a hybrid semi-parametric quantile regression random forest approach to evaluate value at risk (VaR). A Quantile regression random forest is introduced to explain the non-linear relationship in multi-period VaR measurement. Moreover, the essential algorithms and distributions of various holding periods are given. The results show that the generalized autoregressive conditional...
We introduce a class of mixed nonlinear variational inclusion for fuzzy mappings in Hilbert spaces. By using the resolvent operator technique for maximal monotone mapping, we construct some new iterative algorithms for solving this class of variational inclusions. We prove the existence of solution for this kind of variational inclusions and the convergence of iterative sequences generalized by the...
Recent years, context has been identified as an important factor in recommender systems. Great contributions have been done for context-aware collaborative filtering recommendation approaches, but the contextual parameters in current approaches have same weights for all users. In the paper a recommendation approach based on BP neural network is proposed to learn a personal context-aware rating prediction...
Recent researches pay more attention to stock tendency prediction, which various machine learning approaches have been proposed. In this paper, we propose an algorithm to discover self-correlation of stock price in virtue of the notion of time series motifs, by viewing stock price sequences as time series. Generally, time series motif is a pattern appearing frequently in a time sequence, useful to...
According to the problems of the nonlinearity and non norm on dam displacement prediction, the dam displacement mode based on improved ant colony algorithm neural networks was proposed. The binary ant colony algorithm has been brought into the optimization of weights in neural networks. So that the shortcomings of the ant algorithm using in the combinatorial optimization in continuous field have been...
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