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:在包括系统监测、保健和网络安全在内的各个领域,发现时间序列数据中的异常现象很重要。虽然现有方法的丰富使得难以为特定应用选择最适当的方法,但每种方法在发现某些类型的异常现象方面都有其长处。在本研究中,我们比较了复杂程度不同的六种未经监督的异常现象探测方法,以确定更复杂方法是否普遍效果更好,如果某些方法更适合某些类型的异常现象。我们用UCR异常档案评估了方法,这是最近用来检测异常现象的基准数据集。我们在对每种方法的必要超常参数进行调整之后,分析了数据集和异常类型水平的结果。此外,我们评估了每种方法纳入先前关于异常现象的知识的能力,并审查了点法和顺序特征之间的差异。我们的实验表明,典型机器学习方法一般优于各种异常类型中的深层学习方法。