A generalized matrix profile framework with support for contextual series analysis
A generalized matrix profile framework with support for contextual series analysis
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DOI：
10.1016/j.engappai.2020.103487
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发表时间：
2020-04-01
影响因子：
8
通讯作者：
Van Hoecke, Sofie
中科院分区：
文献类型：
--
作者：
De Paepe, Dieter;Vanden Hautte, Sander;Van Hoecke, Sofie
Matrix Profile是一种最先进的时间序列分析技术，可用于医疗保健、机器人和音频等各个领域的基序发现、异常检测、分割等。最近的技术使用矩阵配置文件作为预处理或建模步骤，我们相信有未开发的潜力，推广的方法。我们推导出一个框架，重点是隐式距离矩阵的计算。我们提出这个框架作为系列距离矩阵（SDM）。在这个框架中，距离测量（SDM生成器）和距离处理器（SDM消费者）可以自由组合，从而允许更大的灵活性和更容易的实验。在SDM中，矩阵配置文件只是一种特定配置。我们还介绍了上下文矩阵配置文件（CMP）作为一个新的SDM消费者能够发现重复模式。CMP为数据分析提供了直观的可视化，并可以发现非不一致的异常。我们证明了这两个真实的世界的情况下。CMP是系列分析的多种新技术中的第一种，适合SDM，可以补充矩阵配置文件。
The Matrix Profile is a state-of-the-art time series analysis technique that can be used for motif discovery, anomaly detection, segmentation and others, in various domains such as healthcare, robotics, and audio. Where recent techniques use the Matrix Profile as a preprocessing or modeling step, we believe there is unexplored potential in generalizing the approach. We derived a framework that focuses on the implicit distance matrix calculation. We present this framework as the Series Distance Matrix (SDM). In this framework, distance measures (SDM-generators) and distance processors (SDM-consumers) can be freely combined, allowing for more flexibility and easier experimentation. In SDM, the Matrix Profile is but one specific configuration. We also introduce the Contextual Matrix Profile (CMP) as a new SDM-consumer capable of discovering repeating patterns. The CMP provides intuitive visualizations for data analysis and can find anomalies that are not discords. We demonstrate this using two real world cases. The CMP is the first of a wide variety of new techniques for series analysis that fits within SDM and can complement the Matrix Profile.