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arXiv · 1906.11710

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

David Rushing Dewhurst·Thayer Alshaabi·Dilan Kiley·Michael V. Arnold·Joshua R. Minot·Christopher M. Danforth·Peter Sheridan Dodds
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Abstract

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.

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David Rushing Dewhurst, Thayer Alshaabi, Dilan Kiley, Michael V. Arnold, Joshua R. Minot, Christopher M. Danforth, Peter Sheridan Dodds. 2019-06-27. The shocklet transform: A decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series. https://arxiv.org/abs/1906.11710

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