Bayesian Estimation of Recurrent Changepoints for Signal Segmentation and Anomaly Detection
Christian Reich, Christina Nicolaou, Ahmad Mansour, Kristof Van Laerhoven · 2019
Signal segmentation is a generic task in many time series applications. We propose approaching it via Bayesian changepoint algorithms, i.e., by assigning segments between changepoints. When successive signals show a recurrent change-point pattern, estimating changepoint recurrence is beneficial for two reasons: While recurrent changepoints yield more robust signal segment estimates, non-recurrent changepoints bear valuable information for unsupervised anomaly detection. This study introduces the changepoint recurrence distribution (CPRD) as an empirical estimate of the recurrent behavior of observed changepoints. Two generic methods for incorporating the estimated CPRD into the process of assessing recurrence of future changepoints are suggested. The knowledge of non-recurrent changepoints arising from one of these methods allows additional unsupervised anomaly detection. The quality both of changepoint recurrence estimation via CPRD and of changepoint-related signal segmentation and un-supervised anomaly detection are verified in a proof-of-concept study for two exemplary machine tool monitoring tasks.