  Change-point detection in a time series aims to discover the time points at
which some unknown underlying physical process that generates the time-series
data has changed. We found that existing approaches become less accurate when
the underlying process is complex and generates large varieties of patterns in
the time series. To address this shortcoming, we propose Shape-CD, a simple,
fast, and accurate change point detection method. Shape-CD uses shape-based
features to model the patterns and a conditional neural field to model the
temporal correlations among the time regions. We evaluated the performance of
Shape-CD using four highly dynamic time-series datasets, including the
ExtraSensory dataset with up to 2000 classes. Shape-CD demonstrated improved
accuracy (7-60% higher in AUC) and faster computational speed compared to
existing approaches. Furthermore, the Shape-CD model consists of only hundreds
of parameters and require less data to train than other deep supervised
learning models.
