Research on Financial Data Forecasting Based on Machine Learning under New Productive Forces

In the context of the development of financial markets, the importance of accurate financial data forecasting in risk management and investment decision-making has become paramount. However, due to the inherent complexity and high volatility of financial data, traditional time series models are unable to achieve the desired level of forecast accuracy. In recent years, machine learning has emerged as a key tool for financial forecasting, offering a robust approach to dealing with complex, nonlinear relationships. This paper proposes an innovative hybrid machine learning model combining adaptive feature selection and ensemble learning for financial data prediction. Firstly, the key features pertinent to the prediction task were extracted through the adaptive feature selection module, thus reducing the impact of data noise. Secondly, a weighted combination of multiple basic prediction models is carried out in conjunction with a multilevel ensemble learning architecture, thereby enhancing the robustness and generalisation ability of the prediction. In comparison with the traditional model, the proposed model is better able to capture nonlinear features in the data and is more resistant to overfitting. In light of the inherent heterogeneity and time-dependence of financial data, we have devised a framework that is capable of effectively capturing the distinctive characteristics of such data. The experimental results demonstrate that the proposed hybrid model exhibits superior performance in terms of prediction accuracy and stability, particularly in high-volatility market environments, when compared to existing benchmark models.