Research on the Construction and Application of Mathematical Models of Deep Learning in Financial Time Series Analysis

This paper proposes a financial time series analysis model based on CNN, which aims to capture the potential laws of financial data through deep learning methods and improve the accuracy of stock price prediction and market volatility analysis. Firstly, the data of financial time series are pre-processed, including data normalization, outlier handling and de-noising. Then, a CNN-based analytical model is proposed, which is based on multilayer convolution and pooling, which can effectively extract the local time series features from the data. Experimental results show that the stock price prediction model based on CNN has an accuracy of 91.2 %, which is 13 % higher than the traditional ARIMA model. In the volatility analysis task, the CNN model can better identify the market volatility pattern and has strong robustness. In addition, the CNN model shows significant advantages in dealing with complex nonlinear fluctuations and long-term dependencies. Compared with traditional statistical methods, deep learning methods can better adapt to the dynamic changes of financial market data.