论文标题
语义不和谐：为时间序列寻找异常的本地模式
Semantic Discord: Finding Unusual Local Patterns for Time Series
论文作者
论文摘要
在长期序列中找到异常的子序列是一个非常重要但困难的问题。现有的最新方法一直集中在寻找与其他子序列最不同的子序列；但是，他们没有考虑到包含异常候选者的背景模式。结果，这种方法可能会错过当地异常。我们介绍了一个名为\ textit {语义不和}的新定义，该定义包含了包含异常候选者的较大子序列的上下文信息。我们提出了一种有效的算法，其衍生下限的算法比现实世界数据中的蛮力算法快3个数量级。我们证明，通过广泛的实验，我们的方法显着优于最新方法。我们进一步解释了语义不和谐的解释性。
Finding anomalous subsequence in a long time series is a very important but difficult problem. Existing state-of-the-art methods have been focusing on searching for the subsequence that is the most dissimilar to the rest of the subsequences; however, they do not take into account the background patterns that contain the anomalous candidates. As a result, such approaches are likely to miss local anomalies. We introduce a new definition named \textit{semantic discord}, which incorporates the context information from larger subsequences containing the anomaly candidates. We propose an efficient algorithm with a derived lower bound that is up to 3 orders of magnitude faster than the brute force algorithm in real world data. We demonstrate that our method significantly outperforms the state-of-the-art methods in locating anomalies by extensive experiments. We further explain the interpretability of semantic discord.
