Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics

TL;DR

This paper introduces a new framework combining spatiotemporal permutation entropy with boosted quantile regression neural networks to improve complex system prognostics and pattern prediction accuracy.

Contribution

It integrates entropy analysis with advanced neural networks for probabilistic predictions, enhancing accuracy and robustness in complex, multidimensional systems.

Findings

Achieved 81.17% accuracy in spatiotemporal pattern classification.

Increased critical transition detection accuracy by 79%.

Improved long-term prediction reliability by 81.22%.

Abstract

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. The system leverages dual computational stages: first implementing spatiotemporal entropy extraction optimized for multiscale temporal and spatial data streams, followed by an integrated BEQRNN layer that enables probabilistic pattern prediction with uncertainty quantification. This architecture achieves 81.17% accuracy in spatiotemporal pattern classification with prediction horizons up to 200 time steps and maintains robust performance across diverse regimes. Field testing across chaotic attractors, reaction-diffusion systems, and industrial datasets shows a 79% increase in critical transition detection accuracy and 81.22% improvement in long-term prediction reliability. The framework's effectiveness in processing complex, multimodal entropy features demonstrates significant potential for real-time prognostic applications.