文献类型：会议论文

英文题名：Financial risk forecast analysis based on deep learning considering the data collection from different sources

作者：Yang, Haiping[1]; Hu, Xiaodan[2]

第一作者：Yang, Haiping

机构：[1] School of Finance, Herbin University of Commerce, Heilongjiang, Harbin, 150028, China; [2] Management College, Beijing Union University, Chaoyang, Beijing, 100101, China

第一机构：School of Finance, Herbin University of Commerce, Heilongjiang, Harbin, 150028, China

会议论文集：Proceedings - 4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

会议日期：January 20, 2022 - January 22, 2022

会议地点：Tirunelveli, India

语种：英文

外文关键词：Color - Deep learning - Finance - Forecasting - Risk analysis - Risk assessment - Systems engineering - Time series analysis

摘要：Financial risk forecast analysis based on deep learning considering the data collection from different sources is studied in this paper. This paper combines the time series analysis method of long correlation characteristics with the self-similar business modeling analysis, and gives a method to use the FARIMA model to study the self-similar business. It is found in practice that the reflectance spectra predicted by the classic Ciapper-yuie model are generally dark. This is because according to the modulation transfer function and Gaussian linear propagation function of the paper, the probability of light entering from one color and coming out of the same color is higher than the overall probability of this color. Hence, this kernel is used to optimize the deep learning model. And then, the model is applied to the risk forecast analysis. Through the proper simulations, the performance is validated. ? 2022 IEEE