Adaptive Methods for Activity Monitoring of Streaming Data

Activity monitoring deals with monitoring data (usually streaming data) for interesting events. It has several applications such as building an alarm or an alert system that triggers when outliers or change points are detected. We discuss desiderata for such a system. Then, assuming that the data can be modeled by linear models, we describe an adaptive incremental method for detecting outliers and change points in data streams. Our algorithm uses (a) intuitive criteria for labeling a data point as an outlier or as a change point, and (b) an adaptive incremental model estimation method. In this paper, we use a forgetting factor– based Recursive Least Squares algorithm for adaptive incremental model estimation. We also present experiment results using both simulated and real data, which show that our algorithms for change and outlier detection could accurately detect these events.