Time series motif discovery has been a fundamental task to identify meaningful repeated patterns in time series. Recently, time series chains were introduced as an expansion of time series motifs to identify the continuous evolving patterns in time series data. Informally, a time series chain (TSC) is a temporally ordered set of time series subsequences, in which every subsequence is similar to the one that precedes it, but the last and the first can be arbitrarily dissimilar. TSCs are shown to be able to reveal latent continuous evolving trends in the time series, and identify precursors of unusual events in complex systems. Despite its promising interpretability, unfortunately, we have observed that existing TSC definitions lack the ability to accurately cover the evolving part of a time series: the discovered chains can be easily cut by noise and can include non-evolving patterns, making them impractical in real-world applications. Inspired by a recent work that tracks how the nearest neighbor of a time series subsequence changes over time, we introduce a new TSC definition which is much more robust to noise in the data, in the sense that they can better locate the evolving patterns while excluding the non-evolving ones. We further propose two new quality metrics to rank the discovered chains. With extensive empirical evaluations, we demonstrate that the proposed TSC definition is significantly more robust to noise than the state of the art, and the top ranked chains discovered can reveal meaningful regularities in a variety of real world datasets.
翻译:时间序列 mostif 发现是查明时间序列中有意义重复模式的基本任务。 最近, 时间序列链被引入了时间序列元素的扩展, 以确认时间序列数据中不断演变的模式。 非正式地说, 时间序列链( TSC) 是时间序列序列序列的一组时间序列后继序列, 其中每个后继序列都类似于其前继序列, 但最后和第一个后继都可能任意地不同。 TSC 显示能够揭示时间序列中潜在的持续变化趋势, 并查明复杂系统中异常事件的先兆。 不幸的是, 我们观察到, 尽管时间序列中的时间序列的可有希望的解释性, 以扩大时间序列中不断演变的模式。 时间序列中现有的 TSC 定义缺乏准确覆盖时间序列中不断演变部分的能力: 被发现的链( TSC) 可以很容易被噪音剪切, 并且可以包括非动态模式, 在现实世界应用中不切实际序列中不切实际变化的顺序。 最近一项追踪时间序列中最近邻的顺序随时间序列随时间序列的变化变化, 我们引入一个新的 TSC 定义, 它比数据序列中的噪音更坚固得多, 。 从两个意义上说, 它们可以更好地定位到不断显示不断的路径, 。