论文标题
CPOP：检测分段线性信号的变化
cpop: Detecting changes in piecewise-linear signals
论文作者
论文摘要
更改点检测是许多应用程序域的应用程序的重要问题。人们可能希望检测到许多不同类型的更改，以及用于检测它们的算法和软件。但是，在信号和噪声模型的平均值中检测斜率变化的方法相对较少。我们描述了综合R档案网络（CRAN）上可用的R软件包CPOP。该软件包实现了一种动态编程算法CPOP，以找到最大程度地减少L_0惩罚成本的最佳更改集，其成本为加权剩余的正方形总和。该软件包已扩展了CPOP算法，因此它可以分析间隔不均的数据，允许异质噪声差异，并允许潜在变化位置的网格与数据点的位置不同。还有一个实现方法，它使用农作物算法来检测所有最佳分割，因为您会在连续的值范围内添加更改的L_0惩罚。
Changepoint detection is an important problem with applications across many application domains. There are many different types of changes that one may wish to detect, and a wide-range of algorithms and software for detecting them. However there are relatively few approaches for detecting changes-in-slope in the mean of a signal plus noise model. We describe the R package, cpop, available on the Comprehensive R Archive Network (CRAN). This package implements CPOP, a dynamic programming algorithm, to find the optimal set of changes that minimises an L_0 penalised cost, with the cost being a weighted residual sum of squares. The package has extended the CPOP algorithm so it can analyse data that is unevenly spaced, allow for heterogeneous noise variance, and allows for a grid of potential change locations to be different from the locations of the data points. There is also an implementation that uses the CROPS algorithm to detect all segmentations that are optimal as you vary the L_0 penalty for adding a change across a continuous range of values.
