An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929369162711040 |
|---|---|
| author | Decke, Jens Jenß, Arne Sick, Bernhard Gruhl, Christian |
| author_facet | Decke, Jens Jenß, Arne Sick, Bernhard Gruhl, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00080 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing Decke, Jens Jenß, Arne Sick, Bernhard Gruhl, Christian Machine Learning 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. |
| title | An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.00080 |