# Partially-Observable Sequential Change-Point Detection for Autocorrelated Data via Upper Confidence Region

arXiv: https://arxiv.org/abs/2404.00220 · HTML: https://mlsanity.com/paper/2404.00220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit](https://mlsanity.com/paper/1802.03692/) (2018)
- [A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem](https://mlsanity.com/paper/1711.03539/) (2017)
- [Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits](https://mlsanity.com/paper/1507.04523/) (2015)
- [Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules](https://mlsanity.com/paper/1906.08120/) (2019)
- [Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits](https://mlsanity.com/paper/1709.04004/) (2017)
- [AdaLinUCB: Opportunistic Learning for Contextual Bandits](https://mlsanity.com/paper/1902.07802/) (2019)
- [Optimal Policies for Observing Time Series and Related Restless Bandit Problems](https://mlsanity.com/paper/1703.10010/) (2017)
- [Sequential Changepoint Approach for Online Community Detection](https://mlsanity.com/paper/1407.5978/) (2014)
- [Sketching for Sequential Change-Point Detection](https://mlsanity.com/paper/1505.06770/) (2015)

## Abstract (arXiv)

Sequential change point detection for multivariate autocorrelated data is a very common problem in practice. However, when the sensing resources are limited, only a subset of variables from the multivariate system can be observed at each sensing time point. This raises the problem of partially observable multi-sensor sequential change point detection. For it, we propose a detection scheme called adaptive upper confidence region with state space model (AUCRSS). It models multivariate time series via a state space model (SSM), and uses an adaptive sampling policy for efficient change point detection and localization. A partially-observable Kalman filter algorithm is developed for online inference of SSM, and accordingly, a change point detection scheme based on a generalized likelihood ratio test is developed. How its detection power relates to the adaptive sampling strategy is analyzed. Meanwhile, by treating the detection power as a reward, its connection with the online combinatorial multi-armed bandit (CMAB) problem is formulated and an adaptive upper confidence region algorithm is proposed for adaptive sampling policy design. Theoretical analysis of the asymptotic average detection delay is performed, and thorough numerical studies with synthetic data and real-world data are conducted to demonstrate the effectiveness of our method.