Automatic Adaptive Conformal Inference for Time Series

Student: Artem Makhin

Educational Programme: Math of Machine Learning (Master)

Final Grade: 7

Year of Graduation: 2024

This paper proposes a method for conformal inference in time series. While most works are done under conditions of a constant distribution, the proposed method works with any changes in the data generation process. The method works with any black box forecast models that predict the future values of a time series. We achieve this by modifying the adaptive conformal inference (ACI) algorithm of Gibbs and Candès (2021) with replace constant learning rate parameter on adaptable to changes in data generation process. We tested our method on real datasets and showed the absence of sharp explosions in the width of intervals that exist in modern methods.

Full text (added May 31, 2024)

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses