Matrix Profile Goes MAD：数据序列中的变长模体和不一致发现
Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series
摘要：
在过去的15年里，数据序列主题和不一致发现已经成为数据序列挖掘的两个有用和常用的原语，应用于许多领域，包括机器人，昆虫学，地震学，医学和气候学。然而，最先进的主题和不和谐发现工具仍然需要用户提供相对长度。然而，在一些情况下，长度的选择是关键和无情的。不幸的是，测试给定范围内所有长度的明显的蛮力解决方案在计算上是站不住脚的。在这项工作中，我们引入了一个新的框架，它提供了一个准确的和可扩展的主题和不和谐的发现算法，有效地发现所有的主题和不和谐在一个给定的长度范围内。我们用五个不同的真实数据集来评估我们的方法，并证明它比最先进的方法快20倍。我们的结果还表明，去除用户知道正确长度的不切实际的假设，通常可以产生更直观和可操作的结果，否则可能会错过。（论文发表于《数据采掘与知识发现期刊- 2020》）
英文摘要：
In the last fifteen years, data series motif and discord discovery have emerged as two useful and well-used primitives for data series mining, with applications to many domains, including robotics, entomology, seismology, medicine, and climatology. Nevertheless, the state-of-the-art motif and discord discovery tools still require the user to provide the relative length. Yet, in several cases, the choice of length is critical and unforgiving. Unfortunately, the obvious brute-force solution, which tests all lengths within a given range, is computationally untenable. In this work, we introduce a new framework, which provides an exact and scalable motif and discord discovery algorithm that efficiently finds all motifs and discords in a given range of lengths. We evaluate our approach with five diverse real datasets, and demonstrate that it is up to 20 times faster than the state-of-the-art. Our results also show that removing the unrealistic assumption that the user knows the correct length, can often produce more intuitive and actionable results, which could have otherwise been missed. (Paper published in Data Mining and Knowledge Discovery Journal - 2020)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
