论文标题
机器学习中的顺序假设测试和原油价格跳高尺寸检测
Sequential hypothesis testing in machine learning, and crude oil price jump size detection
论文作者
论文摘要
在本文中，我们提出了一个顺序的假设检验，用于检测一般跳跃大小的分辨率。提出和分析了相应的对数似然比的无限发电机。计算了无限发电机的界限，从超溶液和子分析方面进行计算。对于原油价格数据集的各种分类问题，这表明这是可以实现的。实施了机器和深度学习算法，以从原油数据集中提取特定的确定性组件，并实施确定性组件以改善Barndorff-Nielsen和Shephard模型，Barndorff-Nielsen和Shephard模型是一种常用的衍生品和商品市场分析的随机模型。
In this paper we present a sequential hypothesis test for the detection of general jump size distrubution. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analyzed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the crude oil data set, and the deterministic component is implemented to improve the Barndorff-Nielsen and Shephard model, a commonly used stochastic model for derivative and commodity market analysis.
