The purpose is to effectively manage the financial market, comprehensive assess personal credit, reduce the risk of financial enterprises. Given the systemic risk problem caused by the lack of credit scoring in the existing financial market, a credit scoring model is put forward based on the deep learning network. The proposed model uses RNN (Recurrent Neural Network) and BRNN (Bidirectional Recurrent Neural Network) to avoid the limitations of shallow models. Afterward, to optimize path analysis, bionic optimization algorithms are introduced, and an integrated deep learning model is proposed. Finally, a financial credit risk management system using the integrated deep learning model is proposed. The probability of default or overdue customers is predicted through verification on three real credit data sets, thus realizing the credit risk management for credit customers.
This paper puts forward an ensemble model based on uncertainty simulation and multiple model combination for short-term traffic flow prediction. Firstly, the initial condition of traffic flow be simulated by using a normal distribution, which makes a better description of the real traffic flow situation. Secondly, an ensemble prediction model with different types of prediction methods has been presented in order to catch any possible change of trend in the traffic flow. In the ensemble model, In order to improve the prediction accuracy, this paper put forwards an equivalence test and dispersion adaptability test for choosing the most effective methods in the ensemble system. Finally, a case study be given to show the performance of the ensemble model. Predication result shows that this model has a good performance with freeway traffic flow, It is capable of providing more detail about the traffic volume, it provide mean value and standard deviation value of traffic volume in the next period, which is most important information for traffic managers and travelers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.