Objectives: Deep learning has become the most representative and potential intelligent system modeling technology in artificial intelligence. However, the complexity of financial markets goes far beyond all economic games. Methods: This paper is devoted to the feasibility
and efficiency of the deep-integration neural network model as one of the main paradigms of in-depth learning in the intelligent prediction of financial time. A prediction model of stack self-coding neural network composed of bottom stack self-coding and top regression neurons is proposed.
Results: Firstly, the self-encoder unsupervised learning mechanism is used to identify and learn the time series, and the layers of the neural network are learned greedy layer by layer. Then the stack self-encoder is extended to the SAEP model with supervised mechanism, and the parameters
learned by SAE are used. Used to initialize the neural network, and finally use the supervised learning to fine-tune the weights. Conclusion: The research results show that the model provides effective financial planning and decision-making basis for financial forecasting, maintains
the healthy development of financial markets, and maximizes the benefits of profit-making institutions.
A Hybrid Neuro-Fuzzy Model for Stock Market Time-Series Prediction
