Change-point detection with supervised learning and feature selection

Data streams with high dimensions are more and more common as data sets become wider. Time segments of stable system performance are often interrupted with change events. The change-point problem is to detect such changes and identify attributes that contribute to the change. Existing methods focus on detecting a single (or few) change-point in a univariate (or low-dimensional) process. We consider the important highdimensional multivariate case with multiple change-points and without an assumed distribution. The problem is transformed to a supervised learning problem with time as the output response and the process variables as inputs. This opens the problem to a wide set of supervised learning tools. Feature selection methods are used to identify the subset of variables that change. An illustrative example illustrates the method in an important type of application.