Hongrong Wang1,2, Xinjian Li3,4, Xingjing She1, Wenjian Ma1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572
- 26 November 2025
Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >
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