The need for fast on-line algorithms to analyze high data-rate measurements is a vital element in production settings. Given the ever-increasing number of data sources coupled with increasing complexity of applications, and workload patterns, anomaly detection methods should be light-weight and must operate in real-time. In many modern applications, data arrive in a streaming fashion. Therefore, the underlying assumption of classical methods that the data is a sample from a stable distribution is not valid, and Gaussian and non-parametric based methods such as the control chart and boxplot are inadequate. Streaming data is an ever-changing superposition of distributions. Detection of such changes in real-time is one of the fundamental challenges. We propose low-complexity robust modifications to the conventional Tukey boxplot based on fast highly efficient robust estimates of scale. Results using synthetic as well as real-world data show that our methods outperform the Tukey boxplot and methods based on Gaussian limits.
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