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

## TL;DR

The paper introduces the Discrete Shocklet Transform (DST) and STAR algorithm for identifying local, mechanism-driven dynamics in sociotechnical time series, providing a shape-based, timescale-independent analysis tool that outperforms existing methods.

## Contribution

The authors present a novel shape-based transform and similarity search method that effectively detects local dynamics across multiple timescales in sociotechnical data, with demonstrated application to Twitter data.

## Key findings

- DST identifies mechanism-driven dynamics at various timescales.
- The algorithms are insensitive to parameter choices.
- Application reveals social events and dynamics in Twitter data.

## 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.