Research On Complex Sequence Prediction and Interpretation Based on Adversarial Learning Over Precise Networks and Heuristic Optimization Optimization

Abstract

In the field of high-dimensional dynamic system modeling, achieving high-precision prediction of target variables, interpretability of model structures, and quantification of output uncertainty has always been a key focus of intelligent system research. Traditional methods encounter bottlenecks in terms of feature heterogeneity, multi-dimensional interactions, and prediction credibility. This paper proposes a multi-objective sequence modeling framework that integrates generative adversarial learning, simulated annealing optimization, and a game-theoretic interpretation mechanism. This framework first cleans, standardizes, and imputes missing values in the original structured data, and then reconstructs the input tensor using a BP neural network. Subsequently, a prediction model based on GAN is constructed, and SA is used for global optimization of key hyperparameters. During the prediction phase, a Monte Carlo perturbation strategy is introduced to quantify uncertainty. Finally, the importance of features is analyzed based on the SHAP method to achieve causal transparency. This method outperforms mainstream baseline models such as LSTM and Transformer in multiple metrics, demonstrating good generalization ability and interpretability. It is suitable for prediction and auxiliary decision-making in complex engineering systems in fields such as finance and industry, and has broad application prospects and significant practical value.