PREVIOUS SEMINAR - 19th June 2020
Speaker: Martin Tveten (Dept. of Mathematics, University of Oslo)
Title: Scalable changepoint and anomaly detection in cross-correlated data
Abstract: In the seminar, I will present ongoing work in collaboration with the Statscale group on detecting changes or anomalies in the mean of a subset of variables in cross-correlated data. The maximum likelihood solution of both problems scale exponentially in the number of variables, so not many variables are needed before an approximation is necessary. We propose an approximation in terms of a binary quadratic program and derive a dynamic programming algorithm for computing its solution in linear time in the number of variables, given that the precision matrix is banded. Our simulations indicate that little power is lost by using the approximation in place of the exact maximum likelihood, and that our method performs well even if the sparsity structure of the precision matrix estimate is misspecified. Through the simulation study, we also aim to understand when it is worth the effort to incorporate correlations rather than assuming all variables to be independent, and finding out how our method compares to competing methods in terms of power and estimation accuracy in a range of scenarios. Finally, results from an application of the method to detect known faults on a pump monitored by sensors will be shown.