# Sequential hypothesis testing in machine learning, and crude oil price jump size detection

arXiv: https://arxiv.org/abs/2004.08889 · HTML: https://mlsanity.com/paper/2004.08889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks](https://mlsanity.com/paper/2107.14695/) (2021)

## Abstract (arXiv)

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.