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.

中文翻译：

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Shocklet变换：一种用于识别社会技术时间序列中局部机制驱动的动力学的分解方法

我们介绍了一种定性的，基于形状的，与时间刻度无关的时域变换，该变换用于从社会技术时间序列中提取局部动力学（称为离散Shocklet变换（DST））以及相关的相似性搜索例程，即Shocklet变换和排名（STAR）算法，它指示时间窗口，在此窗口中时间序列的面板显示出质量上类似的异常行为。在将我们的算法与异常检测和时间序列相似性搜索中使用的其他方法（例如矩阵配置文件，季节性混合ESD和基于离散小波变换的过程）区分开来之后，我们证明了DST在广泛的范围内识别机构驱动的动力学的能力时标的范围及其对功能参数化的相对不敏感。作为应用，