Signaling a diverse range of changes in multivariate time series: A flexible kernel-based change point detection approach KU Leuven
Many scientific fields track variables through time to monitor trends, dynamics and abrupt changes. In this dissertation, we focus on the latter and aim to detect sudden distributional changes in time series data. Most of the existing change point detection methods proposed to automatically signal these abrupt shifts are univariate, targeting mean and other univariate statistics. This is an important limitation since in many applications, ...