Summary of Partially-observable Sequential Change-point Detection For Autocorrelated Data Via Upper Confidence Region, by Haijie Xu et al.
Partially-Observable Sequential Change-Point Detection for Autocorrelated Data via Upper Confidence Region
by Haijie Xu, Xiaochen Xian, Chen Zhang, Kaibo Liu
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a sequential change point detection scheme called Adaptive Upper Confidence Region with State Space Model (AUCRSS) for partially observable multi-sensor systems. AUCRSS models multivariate time series using a state space model and employs an adaptive sampling policy for efficient change point detection and localization. The method uses a partially-observable Kalman filter algorithm for online inference of the state space model, and a generalized likelihood ratio test-based scheme for change point detection. The relationship between the detection power and adaptive sampling strategy is analyzed, and the connection with the online combinatorial multi-armed bandit (CMAB) problem is formulated to design an adaptive upper confidence region algorithm 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 AUCRSS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new method called AUCRSS to detect changes in partially observable multi-sensor systems. It’s like trying to find a specific pattern in a big puzzle that’s constantly changing, but you only get to see parts of it at a time. The method uses special math to model the puzzle and figure out what’s happening. It also has a built-in way to decide when to look at more pieces of the puzzle or stop looking altogether. The paper shows how well this method works using fake data and real-world examples. |
Keywords
» Artificial intelligence » Inference » Likelihood » Synthetic data » Time series
