Fast likelihood-based change point detection
This work addresses the computational bottleneck in change point detection for real-time monitoring applications, offering a practical solution for large-scale data streams.
The paper tackles the problem of detecting change points in binary data streams by proposing an approximation algorithm that reduces the time complexity from O(n) to O(ε^{-1} log^2 n) while maintaining near-optimal accuracy, with empirical results showing significant speed-ups and minimal loss in optimality.
Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study change detection in binary streams. To this end, we use a likelihood ratio between two models as a measure for indicating change. The first model is a single bernoulli variable while the second model divides the stored data in two segments, and models each segment with its own bernoulli variable. Finding the optimal split can be done in $O(n)$ time, where $n$ is the number of entries since the last change point. This is too expensive for large $n$. To combat this we propose an approximation scheme that yields $(1 - ε)$ approximation in $O(ε^{-1} \log^2 n)$ time. The speed-up consists of several steps: First we reduce the number of possible candidates by adopting a known result from segmentation problems. We then show that for fixed bernoulli parameters we can find the optimal change point in logarithmic time. Finally, we show how to construct a candidate list of size $O(ε^{-1} \log n)$ for model parameters. We demonstrate empirically the approximation quality and the running time of our algorithm, showing that we can gain a significant speed-up with a minimal average loss in optimality.