The shocklet transform: a decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series

• Published in: EPJ data science Vol. 9; no. 1; pp. 3 - 36
• Main Authors: Dewhurst, David Rushing, Alshaabi, Thayer, Kiley, Dilan, Arnold, Michael V., Minot, Joshua R., Danforth, Christopher M., Dodds, Peter Sheridan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 07-02-2020; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Anomalies; Complexity; Computer Appl. in Social and Behavioral Sciences; Computer Science; Data science; Data-driven Science; Datasets; Discrete Wavelet Transform; Dynamics; Methods; Modeling and Theory Building; Nonparametric statistics; Parameterization; Regular Article; Similarity; Social media; Social networks; Sociotechnical time series; Time series; Time-domain filtering; Typology; Wavelet transforms; Windows (intervals); Sociotechnical time series; Social media; Nonparametric statistics; Time-domain filtering
• ISSN: 2193-1127; 2193-1127
• DOI: 10.1140/epjds/s13688-020-0220-x

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.