论文标题
预算上的在线更改点检测
Online Changepoint Detection on a Budget
论文作者
论文摘要
更改点是数据基础分布的突然变化。检测数据流的变化是许多应用程序的重要问题。在本文中，我们对在在线环境中运行的变更点检测算法感兴趣，因为其存储要求和每个观察结果最差的计算复杂性都与以前的观察次数无关。我们为单变量数据和多变量数据提出了一种在线变更点检测算法，该算法与离线更改点检测算法相比，同时也以严格约束的计算模型进行了比较。此外，我们为这些算法提供了一种简单的在线超参数自动调整技术。
Changepoints are abrupt variations in the underlying distribution of data. Detecting changes in a data stream is an important problem with many applications. In this paper, we are interested in changepoint detection algorithms which operate in an online setting in the sense that both its storage requirements and worst-case computational complexity per observation are independent of the number of previous observations. We propose an online changepoint detection algorithm for both univariate and multivariate data which compares favorably with offline changepoint detection algorithms while also operating in a strictly more constrained computational model. In addition, we present a simple online hyperparameter auto tuning technique for these algorithms.
