Challenges in high dimensional change point analysis and advanced approaches

Albalawi, Shrog; Drikvandi, Reza

Abstract

Change point analysis aims at identifying significant distributional shifts and changes in data sequences. While the problem has been extensively studied for standard low dimensional data, the transition to high dimensional data imposes several challenges. This paper delves into the complexities of change point detection with high dimensional data, discussing the main difficulties associated with high dimensional change point analysis and demonstrating some limitations of the recent methods. The paper also discusses an approach for post detection analysis with high dimensional change points.

Citation

Albalawi, S., & Drikvandi, R. (2024, August). Challenges in high dimensional change point analysis and advanced approaches. Presented at 6th International Conference on Statistics: Theory and Applications (ICSTA'24), Barcelona, Spain

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 6th International Conference on Statistics: Theory and Applications (ICSTA'24)
• Start Date: Aug 19, 2024
• End Date: Aug 21, 2024
• Acceptance Date: Mar 25, 2024
• Online Publication Date: Aug 19, 2024
• Publication Date: Aug 19, 2024
• Deposit Date: Sep 11, 2024
• Publicly Available Date: Sep 13, 2024
• Peer Reviewed: Peer Reviewed
• Book Title: Proceedings of the 6th International Conference on Statistics: Theory and Applications (ICSTA 2024)
• DOI: https://doi.org/10.11159/icsta24.121
• Public URL: https://durham-repository.worktribe.com/output/2860541
• Publisher URL: https://avestia.com/ICSTA2024_Proceedings/files/paper/ICSTA_121.pdf