# Quickest Change Detection with Confusing Change

arXiv: https://arxiv.org/abs/2405.00842 · HTML: https://mlsanity.com/paper/2405.00842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 4 of 4)

- [Quickest Change Detection in Anonymous Heterogeneous Sensor Networks](https://mlsanity.com/paper/2202.13023/) (2022)
- [Adaptive Partially-Observed Sequential Change Detection and Isolation](https://mlsanity.com/paper/2208.08855/) (2022)
- [Quickest Change Detection with Leave-one-out Density Estimation](https://mlsanity.com/paper/2211.00223/) (2022)
- [Sequential Change Diagnosis Revisited and the Adaptive Matrix CuSum](https://mlsanity.com/paper/2211.12980/) (2022)

## Abstract (arXiv)

In the problem of quickest change detection (QCD), a change occurs at some unknown time in the distribution of a sequence of independent observations. This work studies a QCD problem where the change is either a bad change, which we aim to detect, or a confusing change, which is not of our interest. Our objective is to detect a bad change as quickly as possible while avoiding raising a false alarm for pre-change or a confusing change. We identify a specific set of pre-change, bad change, and confusing change distributions that pose challenges beyond the capabilities of standard Cumulative Sum (CuSum) procedures. Proposing novel CuSum-based detection procedures, S-CuSum and J-CuSum, leveraging two CuSum statistics, we offer solutions applicable across all kinds of pre-change, bad change, and confusing change distributions. For both S-CuSum and J-CuSum, we provide analytical performance guarantees and validate them by numerical results. Furthermore, both procedures are computationally efficient as they only require simple recursive updates.