Multi-Objective System Predicting Based on Hybrid LSTM-Markov and GSRF Models - Lin, Yichen (Transactions on Computer Science and Intelligent Systems Research, 2025)
The prediction of multi-objective complex systems has always been one of the significant challenges in academia. The multi-dimensional structure, diverse distribution, and complex temporal dependencies of the data make it difficult for conventional mathematical models to perform effective analysis. This study explores the application of hybrid machine learning algorithms in predicting multi-objective systems. This paper developed an LSTM-Markov model which uses the Markov chain to optimize the preliminary predictive results obtained by LSTM. A Grid Search-optimized Random Forest (GSRF) model was integrated with the LSTM-Markov model to address the zero-inflated and long-tailed characteristics of the chosen datasets. The GSRF model was a powerful non-linear classifier that could resolve the zero-inflated outputs. Using Olympic medal counts prediction as the case, the final evaluation metrics of LSTM-Markov revealed that the MSE and R-squared value of the LSTM-Markov model were 0.12 and 0.89 respectively, indicating significant improvements upon baseline methods. The classification accuracy of GSRF achieved 95.52%, demonstrating promising performance in classification and forecasting. This study provides an innovative approach to solving the multi-target predicting problems of complex time series systems. The method also holds significant application potential in areas such as financial risk prediction and disease transmission modeling.
Keywords: Multi-objective system; LSTM, Markov chain; Zero-inflation; Grid Search Random Forest.