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

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

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,...

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Bibliographic Details
Main Authors: David Rushing Dewhurst, Thayer Alshaabi, Dilan Kiley, Michael V. Arnold, Joshua R. Minot, Christopher M. Danforth, Peter Sheridan Dodds
Format: Article
Language:English
Published: SpringerOpen 2020-02-01
Series:EPJ Data Science
Subjects:
Nonparametric statistics
Sociotechnical time series
Time-domain filtering
Social media
Online Access:https://doi.org/10.1140/epjds/s13688-020-0220-x

Internet

https://doi.org/10.1140/epjds/s13688-020-0220-x

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