论文标题
污染下的离线变更检测
Offline Change Detection under Contamination
论文作者
论文摘要
在这项工作中，我们提出了一种非参数和鲁棒的变更检测算法，以检测污染下的时间序列数据中的多个变更点。污染模型足够通用，因为这是一种特殊情况。同样，污染模型是遗忘和任意的。更改检测算法是为离线设置而设计的，该目标是在接收到所有数据时检测更改。我们只对插入器（未腐烂的数据）进行薄弱的时刻假设，以处理大量分布。该算法中强大的扫描统计量是根据影响功能的平均估计器来形成的。随着样品数量的增加，我们建立了估计的变更点索引的一致性，并提供了经验证据以支持一致性结果。
In this work, we propose a non-parametric and robust change detection algorithm to detect multiple change points in time series data under contamination. The contamination model is sufficiently general, in that, the most common model used in the context of change detection -- Huber contamination model -- is a special case. Also, the contamination model is oblivious and arbitrary. The change detection algorithm is designed for the offline setting, where the objective is to detect changes when all data are received. We only make weak moment assumptions on the inliers (uncorrupted data) to handle a large class of distributions. The robust scan statistic in the algorithm is fashioned using mean estimators based on influence functions. We establish the consistency of the estimated change point indexes as the number of samples increases, and provide empirical evidence to support the consistency results.
