论文标题
通过部分衍生物的深度非交叉分位数
Deep Non-Crossing Quantiles through the Partial Derivative
论文作者
论文摘要
分位数回归（QR）提供了一种近似单个条件分位数的方法。为了对条件分布有更有信息的描述，可以将QR与深度学习技术合并，以同时估计多个分位数。但是，QR-loss函数的最小化并不能保证非交叉分位数，这会影响此类预测的有效性，并在某些情况下引入了一个关键问题。在本文中，我们提出了一种通用的深度学习算法，用于预测任意数量的分位数，以确保对机器精度的分位数单调性约束，并保持其相对于替代模型的建模性能。在获得最新结果的几个现实世界数据集上评估了所述的方法，并表明它扩展到大型数据集。
Quantile Regression (QR) provides a way to approximate a single conditional quantile. To have a more informative description of the conditional distribution, QR can be merged with deep learning techniques to simultaneously estimate multiple quantiles. However, the minimisation of the QR-loss function does not guarantee non-crossing quantiles, which affects the validity of such predictions and introduces a critical issue in certain scenarios. In this article, we propose a generic deep learning algorithm for predicting an arbitrary number of quantiles that ensures the quantile monotonicity constraint up to the machine precision and maintains its modelling performance with respect to alternative models. The presented method is evaluated over several real-world datasets obtaining state-of-the-art results as well as showing that it scales to large-size data sets.
