An Efficient Online Monitoring Method for High-Dimensional Data Streams

Monitoring high-dimensional data streams has become increasingly important for real-time detection of abnormal activities in many data-rich applications. We are interested in detecting an occurring event as soon as possible, but we do not know which subset of data streams is affected by the event. By connecting to the problem of detecting heterogenous mixtures, a new control chart is developed based on a powerful goodness-of-fit test of the local cumulative sum statistics from each data stream. Numerical results show that the proposed method is able to balance the detection of various fractions of affected streams, and generally outperforms existing methods. Supplementary materials for this article are available online.