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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

EPJ Data Science, 9, 3, 2020

Times cited: 11

Abstract:

We introduce an unsupervised pattern recognition algorithm termed the Discrete Shocklet Transform (DST) by which local dynamics of time series can be extracted. Time series that are hypothesized 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:

@Article{dewhurst2020b,
  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},
  journal =	 {EPJ Data Science},
  year =	 {2020},
  key =		 {time series,sociotechnical
                  systems,shocks,transforms},
  volume =	 {9},
  doi =		 {10.1140/epjds/s13688-020-0220-x},
  pages =	 {3},
}
 
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