  Change detection (CD) in time series data is a critical problem as it reveal
changes in the underlying generative processes driving the time series. Despite
having received significant attention, one important unexplored aspect is how
to efficiently utilize additional correlated information to improve the
detection and the understanding of changepoints. We propose hierarchical
quickest change detection (HQCD), a framework that formalizes the process of
incorporating additional correlated sources for early changepoint detection.
The core ideas behind HQCD are rooted in the theory of quickest detection and
HQCD can be regarded as its novel generalization to a hierarchical setting. The
sources are classified into targets and surrogates, and HQCD leverages this
structure to systematically assimilate observed data to update changepoint
statistics across layers. The decision on actual changepoints are provided by
minimizing the delay while still maintaining reliability bounds. In addition,
HQCD also uncovers interesting relations between changes at targets from
changes across surrogates. We validate HQCD for reliability and performance
against several state-of-the-art methods for both synthetic dataset (known
changepoints) and several real-life examples (unknown changepoints). Our
experiments indicate that we gain significant robustness without loss of
detection delay through HQCD. Our real-life experiments also showcase the
usefulness of the hierarchical setting by connecting the surrogate sources
(such as Twitter chatter) to target sources (such as Employment related
protests that ultimately lead to major uprisings).
