Financial risk forecast analysis based on deep learning considering the data collection from different sources

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.