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

2 code implementations • 27 Jun 2019 • David Rushing Dewhurst, Thayer Alshaabi, Dilan Kiley, Michael V. Arnold, Joshua R. Minot, Christopher M. Danforth, Peter Sheridan Dodds

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.

Physics and Society Data Structures and Algorithms Signal Processing Data Analysis, Statistics and Probability

The emotional arcs of stories are dominated by six basic shapes

2 code implementations • 24 Jun 2016 • Andrew J. Reagan, Lewis Mitchell, Dilan Kiley, Christopher M. Danforth, Peter Sheridan Dodds

Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture's evolution through its texts using a "big data" lens.