School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
PLoS One. 2023 Aug 16;18(8):e0290092. doi: 10.1371/journal.pone.0290092. eCollection 2023.
Automatic detection of subsequence anomalies (i.e., an abnormal waveform denoted by a sequence of data points) in time series is critical in a wide variety of domains. However, most existing methods for subsequence anomaly detection often require knowing the length and the total number of anomalies in time series. Some methods fail to capture recurrent subsequence anomalies due to using only local or neighborhood information for anomaly detection. To address these limitations, in this paper, we propose a novel graph-represented time series (GraphTS) method for discovering subsequence anomalies. In GraphTS, we provide a new concept of time series graph representation model, which represents both recurrent and rare patterns in a time series. Particularly, in GraphTS, we develop a new 2D time series visualization (2Dviz) method, which compacts all 1D time series patterns into a 2D spatial temporal space. The 2Dviz method transfers time series patterns into a higher-resolution plot for easier sequence anomaly recognition (or detecting subsequence anomalies). Then, a Graph is constructed based on the 2D spatial temporal space of time series to capture recurrent and rare subsequence patterns effectively. The represented Graph also can be used to discover single and recurrent subsequence anomalies with arbitrary lengths. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency.
在许多领域中，时间序列中子序列异常（即异常波形表示为一系列数据点）的自动检测至关重要。然而，大多数现有的子序列异常检测方法通常需要知道时间序列中子序列的长度和总数。由于仅使用局部或邻域信息进行异常检测，一些方法无法捕获重复出现的子序列异常。为了解决这些限制，在本文中，我们提出了一种用于发现子序列异常的新的基于图的时间序列（GraphTS）方法。在 GraphTS 中，我们提供了一种新的时间序列图表示模型的概念，该模型表示时间序列中的重复和罕见模式。特别地，在 GraphTS 中，我们开发了一种新的 2D 时间序列可视化（2Dviz）方法，该方法将所有 1D 时间序列模式压缩到 2D 时空空间中。2Dviz 方法将时间序列模式转换为更高分辨率的图，以便更容易识别序列异常（或检测子序列异常）。然后，基于时间序列的 2D 时空空间构建图，以有效地捕获重复和罕见的子序列模式。表示的图还可用于发现任意长度的单个和重复的子序列异常。实验结果表明，该方法在准确性和效率方面优于最先进的方法。
