With the increasing complexity of financial markets, the performance of traditional statistical methods for time series prediction has been unable to meet the real-time and accuracy requirements. Artificial intelligence technologies, especially models such as deep learning, have been continuously expanded in the application of financial time series modeling, but their prediction performance is still constrained by many factors. This paper systematically analyzes the core bottlenecks of AI models in financial time series prediction, such as obstacles in capturing nonlinear dynamic structures, low out-of-sample generalization ability, and susceptibility to noise interference in the training process. Based on this foundation, a set of performance improvement strategies combining structural optimization, data enhancement processing and integrated learning mechanisms are formulated. By building hybrid models (such as CNN-LSTM, Attention nested Transformer), introducing time series data enhancement methods based on distribution reconstruction, and designing a comprehensive framework of multi-objective loss functions, the stability and accuracy of predictions are greatly enhanced. This study not only provides a reproducible optimization solution for the engineering practice of financial time series prediction models, but also lays a methodological foundation for building AI financial systems with stronger generalization and interpretability in the future.

Xinxing Luo. Performance Improvement and Solution Optimization in AI-Driven Financial Time Series Forecasting. Socio-Economic Statistics Research (2025), Vol. 6, Issue 1: 33-42. https://doi.org/10.38007/SESR.2025.060104.

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