AI RESEARCH
Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics
arXiv CS.LG
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ArXi:2507.14194v2 Announce Type: replace-cross This paper presents a novel framework for pattern prediction and system prognostics centered on Spatiotemporal Permutation Entropy analysis integrated with Boosted Enhanced Quantile Regression Neural Networks (BEQRNNs). We address the challenge of understanding complex dynamical patterns in multidimensional systems through an approach that combines entropy-based complexity measures with advanced neural architectures.