论文标题
高维更改点检测：完整的图形方法
High dimensional change-point detection: a complete graph approach
论文作者
论文摘要
在线更改点检测的目的是准确，及时发现结构中断。随着数据维度的观察数量，在线检测变得具有挑战性。现有方法通常仅测试平均值的变化，这省略了变化变化的实际方面。我们提出了一种完整的基于图的更改点检测算法，以检测具有可变扫描窗口的均值变化和从低维在线数据到高维在线数据的变化。受完整的图形结构的启发，我们将跨度比率引入将高维数据映射到指标中，然后在统计上测试均值变化或方差的变化。理论研究表明，我们的方法具有理想的关键特性，并且具有规定的错误概率。我们证明，该框架在检测能力方面优于其他方法。我们的方法具有很高的检测功率，具有小和多扫描窗口，可以及时检测在线设置中的变更点。最后，我们将该方法应用于财务数据，以检测标准普尔500股股票中的变更点。
The aim of online change-point detection is for a accurate, timely discovery of structural breaks. As data dimension outgrows the number of data in observation, online detection becomes challenging. Existing methods typically test only the change of mean, which omit the practical aspect of change of variance. We propose a complete graph-based, change-point detection algorithm to detect change of mean and variance from low to high-dimensional online data with a variable scanning window. Inspired by complete graph structure, we introduce graph-spanning ratios to map high-dimensional data into metrics, and then test statistically if a change of mean or change of variance occurs. Theoretical study shows that our approach has the desirable pivotal property and is powerful with prescribed error probabilities. We demonstrate that this framework outperforms other methods in terms of detection power. Our approach has high detection power with small and multiple scanning window, which allows timely detection of change-point in the online setting. Finally, we applied the method to financial data to detect change-points in S&P 500 stocks.
