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

Abstract

This paper presents an integrative prognostic framework that combines Spatiotemporal Permutation Entropy (STPE), Boosted Enhanced Quantile Regression Neural Networks (B-EQRNNs), Gated Temporal Attention, a Spiking Neural Network (SNN) refinement stage, and a Temporal Fusion Transformer (TFT) classifier. The motivation is long-horizon fault prediction in distributed industrial electronic systems, where single-sensor or point-estimate models can miss weak spatially propagating degradation signatures and provide limited uncertainty information. The proposed pipeline first converts 70-channel sensor streams into multiscale STPE descriptors, then learns conditional quantile representations and attention-weighted temporal context before final Normal/Abnormal classification. Evaluation is reported on a nine-system industrial electronic-sensor dataset with 48-, 90-, and 168-hour prediction horizons. The comparison includes a tree-based LightGBM baseline and modern sequence baselines available under the same preprocessing protocol, including LSTM, Autoformer, and TCN models. The full pipeline reaches 81.17% accuracy at the 168-hour horizon and is evaluated with component ablations, computational-cost analysis, and an explicit reproducibility protocol. The contribution is therefore framed as a validated hybrid architecture for uncertainty-aware spatiotemporal prognostics rather than as a new standalone learning theory.