An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing

TL;DR

The paper introduces SCQRNN, a novel neural network model that effectively prevents quantile crossing, improves computational efficiency, and converges faster, enhancing reliability and interpretability in quantile regression tasks across various scientific fields.

Contribution

The paper proposes the SCQRNN model with ad hoc sorting to prevent quantile crossing and achieve faster convergence, advancing quantile regression methods.

Findings

Prevents quantile crossing effectively.

Reduces computational complexity and speeds up convergence.

Enhances applications in finance, meteorology, climate science, and engineering.

Abstract

This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.