Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds
Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds
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矩阵配置文件 XI：SCRIMP：以交互速度发现时间序列基序
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Eamonn J. Keogh
中科院分区：
文献类型：
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作者：
Yan Zhu;Chin;Zachary Schall;Kaveh Kamgar;Eamonn J. Keogh
时间序列图案发现是时间序列分析的重要原始性，用于神经科学，音乐和体育分析等多样性。近年来，算法进步（加上硬件改进）极大地扩展了主题发现的权限。然而，我们认为有无限的需要进一步的可扩展性。这是因为与大多数类型的分析相比，基序发现受到交互作用受益。找到图案的两种最先进的算法是Stomp，它需要O（n2）时间和邮票，尽管它是O（logn）因子慢，但它是大多数应用程序的首选解决方案，因为它是快速收敛的任何时间算法。在有利的方案中，邮票只需要将邮票运行到一小部分完成，即可提供非常准确的Top-K图案的近似值。在这项工作中，我们介绍了SCRIMP ++，这是一种O（N2）时间算法，它也是任何时间算法，结合了Stomp和Stamp的最佳功能。正如我们将显示的那样，Scrimp ++保持原始算法的所有理想属性，但是在几乎所有场景中都会在花费完整计算时间的一小部分后产生正确的输出。我们认为，对于许多最终用户，这允许在交互式会话中进行主题发现。此外，这种交互性可以根据可以执行的分析来改变游戏。
Time series motif discovery is an important primitive for time series analytics, and is used in domains as diverse as neuroscience, music and sports analytics. In recent years, algorithmic advances (coupled with hardware improvements) have greatly expanded the purview of motif discovery. Nevertheless, we argue that there is an insatiable need for further scalability. This is because more than most types of analytics, motif discovery benefits from interactivity. The two state-of-the-art algorithms to find motifs are STOMP, which requires O(n2) time, and STAMP, which, despite being an O(logn) factor slower, is the preferred solution for most applications, as it is a fast converging anytime algorithm. In favorable scenarios STAMP needs only to be run to a small fraction of completion to provide a very accurate approximation of the top-k motifs. In this work we introduce SCRIMP++, an O(n2) time algorithm that is also an anytime algorithm, combining the best features of STOMP and STAMP. As we shall show, SCRIMP++ maintains all the desirable properties of the original algorithms, but converges much faster, in almost all scenarios producing the correct output after spending a tiny fraction of the full computation time. We argue that for many end-users, this allows motif discovery to be performed in interactive sessions. Moreover, this interactivity can be game changing in terms of the analytics that can be performed.