  We introduce a qualitative, shape-based, timescale-independent time-domain
transform used to extract local dynamics from sociotechnical time
series---termed the Discrete Shocklet Transform (DST)---and an associated
similarity search routine, the Shocklet Transform And Ranking (STAR) algorithm,
that indicates time windows during which panels of time series display
qualitatively-similar anomalous behavior. After distinguishing our algorithms
from other methods used in anomaly detection and time series similarity search,
such as the matrix profile, seasonal-hybrid ESD, and discrete wavelet
transform-based procedures, we demonstrate the DST's ability to identify
mechanism-driven dynamics at a wide range of timescales and its relative
insensitivity to functional parameterization. As an application, we analyze a
sociotechnical data source (usage frequencies for a subset of words on Twitter)
and highlight our algorithms' utility by using them to extract both a typology
of mechanistic local dynamics and a data-driven narrative of socially-important
events as perceived by English-language Twitter.
