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

D. R. Dewhurst, T. Alshaabi, D. Kiley, M. V. Arnold, J. R. Minot, C. M. Danforth, and P. S. Dodds

Times cited: 1

Abstract:

We introduce an unsupervised pattern recognition algorithm termed the Discrete Shocklet Trans- form (DST) by which local dynamics of time series can be extracted. Time series that are hypothe- sized to be generated by underlying deterministic mechanisms have significantly different DSTs than do purely random null models. We apply the DST to a sociotechnical data source, usage frequencies for a subset of words on Twitter over a decade, and demonstrate the ability of the DST to filter high-dimensional data and automate the extraction of anomalous behavior.
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BibTeX:

@Misc{dewhurst2019a,
  author = 	 {Dewhurst, David Rushing and Alshaabi, Thayer and Kiley, Dilan and Arnold, Michael V. and Minot, Joshua R. and Danforth, Christopher M. and Dodds, Peter Sheridan},
  title = 	 {The shocklet transform: {A} decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series},
  year = 	 {2019},
  note = 	 {Available online at \href{https://arxiv.org/abs/1906.11710}{https://arxiv.org/abs/1906.11710}},
}
 
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