Financial Time Series Data Analysis Based on Data Mining

Based on the in-depth study of data mining, great likelihood estimation, sequential parameter learning and other models, the study designs a sequential Bayesian learning method to estimate the asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) model for the analysis of financial time-series data, aiming to improve its analytical assessment and forecasting performance. The study fully considers the leverage effect, accurately describes the negative correlation between return and volatility, and successfully solves the complex numerical problems in the estimation of stock simulation models. Through comprehensive simulation analysis, the results show that the model can excellently simulate the stock volatility and price trends with remarkable effectiveness, which provides a powerful support for the research and decision-making in the financial market.