Alapfogalmak
The Kernel-based Cumulative Sum (KCUSUM) algorithm is a non-parametric extension of the traditional Cumulative Sum (CUSUM) method, which can effectively detect changes in real-time data streams without requiring prior knowledge of the underlying data distribution.
Kivonat
The content introduces the Kernel-based Cumulative Sum (KCUSUM) algorithm, a non-parametric change point detection method that combines the properties of the CUSUM algorithm with the Maximum Mean Discrepancy (MMD) framework.
Key highlights:
KCUSUM is designed to detect changes in real-time data streams, particularly in high-volume data scenarios, without requiring knowledge of the underlying data distribution.
It compares incoming samples directly with reference samples and computes a statistic based on the MMD non-parametric framework, which allows it to handle scenarios where only reference samples are available.
The MMD-based approach extends KCUSUM's applicability to a wider range of use cases, such as detecting deviations from reference samples in atomic trajectories of proteins in vacuum.
Theoretical analysis of KCUSUM's performance, including metrics like expected delay and mean runtime to false alarms, is provided.
Real-world use cases from scientific simulations, such as NWChem CODAR and protein folding data, are discussed to demonstrate KCUSUM's practical effectiveness in online change point detection.