论文标题
在线时间序列序列异常检测与状态空间高斯流程
Online Time Series Anomaly Detection with State Space Gaussian Processes
论文作者
论文摘要
我们提出了R-SSGPFA，这是一种无监督的在线异常检测模型，用于在高斯流程的有效状态空间配方上建立单元和多元时间序列。对于高维时间序列，我们提出了高斯过程因子分析的扩展，以确定时间序列的常见潜在过程，从而使我们能够以可解释的方式有效地检测异常。通过对从潜在到观察到的映射施加正交性约束，我们可以在加速计算时获得解释性。在遇到异常观察时，使用一个简单的启发式启发式来跳过Kalman更新，从而提高了我们的模型的鲁棒性。我们研究了模型在合成数据上的行为，并在标准基准数据集上显示我们的方法具有最先进的方法，同时便宜。
We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes. For high-dimensional time series, we propose an extension of Gaussian process factor analysis to identify the common latent processes of the time series, allowing us to detect anomalies efficiently in an interpretable manner. We gain explainability while speeding up computations by imposing an orthogonality constraint on the mapping from the latent to the observed. Our model's robustness is improved by using a simple heuristic to skip Kalman updates when encountering anomalous observations. We investigate the behaviour of our model on synthetic data and show on standard benchmark datasets that our method is competitive with state-of-the-art methods while being computationally cheaper.
