The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code
The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code
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时间序列数据挖掘的瑞士军刀：使用矩阵配置文件和十行代码可以做的十件有用的事情
DOI：
10.1007/s10618-019-00668-6
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发表时间：
2020
影响因子：
4.8
通讯作者：
E. Keogh
中科院分区：
文献类型：
--
作者：
Yan Zhu;Shaghayegh Gharghabi;Diego Furtado Silva;Hoang Anh Dau;Chin;N. S. Senobari;Abdulaziz Almaslukh;Kaveh Kamgar;Zachary Zimmerman;G. Funning;A. Mueen;E. Keogh
最近引入的数据结构 Matrix Profile 通过记录每个子序列的位置和到最近邻居的距离来注释时间序列。这些信息简单地提供了对时间序列主题和时间序列不一致的查询的答案，这可能是时间序列数据挖掘中最常用的两个原语。矩阵配置文件的一个吸引人的功能是，它将所执行的分析的高级细节与计算“繁重的工作”完全分离。可以使用适合手头任务的计算范例来计算矩阵配置文件：CPU、GPU、FPGA、分布式计算、随时计算、增量计算等。然而，这种计算的所有细节都可以对分析师隐藏，分析师只需要考虑她的分析需求。在这项工作中，我们扩展了这一理念并提出以下问题：如果我们假设我们免费获得 Matrix Profile，那么我们最多可以编写十行代码来进行哪些有趣的分析？正如我们将要展示的，答案令人惊讶地庞大且多样化。我们的目标不是建立或与最先进的结果竞争，而只是为了表明我们既可以重现许多现有算法的结果，又可以毫不费力地在时间序列数据收集中找到新的规律。
The recently introduced data structure, the Matrix Profile, annotates a time series by recording the location of and distance to the nearest neighbor of every subsequence. This information trivially provides answers to queries for both time series motifs and time series discords, perhaps two of the most frequently used primitives in time series data mining. One attractive feature of the Matrix Profile is that it completely divorces the high-level details of the analytics performed, from the computational “heavy lifting.” The Matrix Profile can be computed using the appropriate computational paradigm for the task at hand: CPU, GPU, FPGA, distributed computing, anytime computation, incremental computation, and so forth. However, all the details of such computation can be hidden from the analyst who only needs to think about her analytical need. In this work, we expand on this philosophy and ask the following question: If we assume that we get the Matrix Profile for free, what interesting analytics can we do, writing at most ten lines of code? As we will show, the answer is surprisingly large and diverse. Our aim here is not to establish or compete with state-of-the-art results, but merely to show that we can both reproduce the results of many existing algorithms and find novel regularities in time series data collections with very little effort.
影响因子：
5.7
作者：
Kurpiewski, MR;Engler, LE;Jen-Jacobson, L
通讯作者：
Jen-Jacobson, L