Research on the Application of Random Forest Algorithm in High-Dimensional Nonlinear System Prediction

Abstract

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This study constructs a predictive model based on the random forest algorithm, focusing on the challenge of predicting outcomes in complex systems. The research first performs time-series structured organization of historical data and extracts key features. A dynamic time-segmentation strategy is employed to divide the dataset into training and testing sets. Model parameters are optimized using information gain calculations and the cross-entropy loss function, enabling efficient capture of nonlinear relationships in high-dimensional data. The model uses key variables such as quantitative features and trend changes as inputs for predictive analysis, and incorporates a logistic regression model to hierarchically refine the results, forming a multi-model collaborative predictive framework. This study leverages the complementary advantages of multi-dimensional data processing workflows and algorithms to establish a predictive framework tailored for complex systems. It demonstrates significant adaptability in high-dimensional nonlinear data scenarios, providing a methodological framework with both theoretical value and practical significance for predictive analysis and feature variable impact assessment in similar complex systems.