In this paper, a new financial data analysis framework named TS-Adapt is proposed which exploits intelligent time series modeling and adaptive algorithms in order to overcome the shortcomings of conventional approaches when dealing with dynamic markets. TS-Adapt constructs a closed-loop learning framework that integrates feature extraction, prediction; and RL-based optimization. In terms of theoretical significance, we bridge the gap between fixed models in finance and deep learning approaches; technically, combining GAN with Proximal Policy Optimization (PPO) makes the model more flexible and robust against severe events in markets. Empirically, our TS-Adapt outperforms all the baselines in reducing MSE by 18.4% and achieving a Sharpe ratio of 1.25 with a controlled maximum drawdown of −15.3%, which are indicative of strong risk-adjusted returns. The ablation studies show that the adaptive module is essential for helping the model understand market states, self-tune parameters, and generate robustness and accuracy under extreme volatility financial conditions. As a practical application, TS-Adapt further provides transparent support with high adaptability for quantitative investment and intelligent risk control, improving robustness of financial system.