论文标题
时间序列通过累积ra特征检测
Time Series Anomaly Detection by Cumulative Radon Features
论文作者
论文摘要
检测异常时间序列是科学，医学和工业任务的关键，但由于其固有的无监督性质而具有挑战性。近年来，通过学习越来越复杂的功能，通常使用深层神经网络，在这项任务上取得了进展。在这项工作中，我们认为，与分配距离测量相结合时，浅特征就足够了。我们的方法模型每个时间序列是特征的高维经验分布，每个时间点构成一个样本。因此，对测试时间序列和正常训练组之间的距离进行建模需要有效测量多元概率分布之间的距离。我们表明，通过使用累积ra rapon特征对每个时间序列进行参数化，我们能够有效地对正常时间序列的分布进行建模。在多个数据集上评估了我们理论上扎根但简单实施的方法，并显示出比建立的经典方法以及复杂的，最先进的深度学习方法更好的结果。提供了代码。
Detecting anomalous time series is key for scientific, medical and industrial tasks, but is challenging due to its inherent unsupervised nature. In recent years, progress has been made on this task by learning increasingly more complex features, often using deep neural networks. In this work, we argue that shallow features suffice when combined with distribution distance measures. Our approach models each time series as a high dimensional empirical distribution of features, where each time-point constitutes a single sample. Modeling the distance between a test time series and the normal training set therefore requires efficiently measuring the distance between multivariate probability distributions. We show that by parameterizing each time series using cumulative Radon features, we are able to efficiently and effectively model the distribution of normal time series. Our theoretically grounded but simple-to-implement approach is evaluated on multiple datasets and shown to achieve better results than established, classical methods as well as complex, state-of-the-art deep learning methods. Code is provided.
