Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare, and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we compare six unsupervised anomaly detection methods of varying complexity to determine whether more complex methods generally perform better and if certain methods are better suited to certain types of anomalies. We evaluated the methods using the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We analyzed the results on a dataset and anomaly type level after adjusting the necessary hyperparameters for each method. Additionally, we assessed the ability of each method to incorporate prior knowledge about anomalies and examined the differences between point-wise and sequence-wise features. Our experiments show that classical machine learning methods generally outperform deep learning methods across a range of anomaly types.

翻译：检测时间序列数据中的异常在系统监控、医疗保健和网络安全等多个领域具有重要意义。尽管现有方法繁多，使得为特定应用选择最合适的方法变得困难，但每种方法在检测特定类型的异常时都有其优势。本研究比较了六种复杂度各异的无监督异常检测方法，以确定更复杂的方法是否通常表现更好，以及某些方法是否更适用于特定类型的异常。我们使用最新的异常检测基准数据集UCR异常档案对方法进行评估。在调整每种方法的必要超参数后，我们从数据集和异常类型两个层面分析了结果。此外，我们评估了每种方法融入异常先验知识的能力，并考察了点级特征与序列级特征之间的差异。实验表明，在多种异常类型中，传统机器学习方法通常优于深度学习方法。