论文标题
单变量时间序列中的异常检测：有关最新的调查
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art
论文作者
论文摘要
长期以来，时间序列数据的异常检测一直是一个重要的研究领域。关于异常检测方法的开创性工作一直集中在统计方法上。近年来，已经开发了越来越多的机器学习算法来检测时间序列异常。随后，研究人员试图使用（深）神经网络来改进这些技术。鉴于越来越多的异常检测方法，研究体缺乏对统计，机器学习和深度学习方法的广泛比较评估。本文研究了所有这三个类别的20个单变量检测方法。该评估是在公开可用的数据集上进行的，该数据集是时间序列异常检测的基准。通过分析每种方法的准确性以及算法的计算时间，我们提供了有关这些异常检测方法性能的详细见解，以及某些适用于某种类型的数据的一般概念。
Anomaly detection for time-series data has been an important research field for a long time. Seminal work on anomaly detection methods has been focussing on statistical approaches. In recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Subsequently, researchers tried to improve these techniques using (deep) neural networks. In the light of the increasing number of anomaly detection methods, the body of research lacks a broad comparative evaluation of statistical, machine learning and deep learning methods. This paper studies 20 univariate anomaly detection methods from the all three categories. The evaluation is conducted on publicly available datasets, which serve as benchmarks for time-series anomaly detection. By analyzing the accuracy of each method as well as the computation time of the algorithms, we provide a thorough insight about the performance of these anomaly detection approaches, alongside some general notion of which method is suited for a certain type of data.
