论文标题
值得吗？比较时间序列中无监督异常检测的六种深度和经典方法
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
论文作者
论文摘要
在时间序列数据中检测异常在各种领域中很重要，包括系统监测，医疗保健和网络安全。虽然丰富的可用方法使得很难为给定应用程序选择最合适的方法，但每种方法都具有检测某些类型异常的优势。在这项研究中，我们比较了六种无监督的异常检测方法的复杂性不同，以确定更复杂的方法是否通常更好地表现更好，并且是否更适合某些类型的异常方法。我们使用UCR异常存档（用于异常检测的最新基准数据集）评估了这些方法。在调整每种方法必要的超参数后，我们在数据集和异常类型水平上分析了结果。此外，我们评估了每种方法合并有关异常的先验知识的能力，并检查了点和序列特征之间的差异。我们的实验表明，经典的机器学习方法通常超过各种异常类型的深度学习方法。
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.
