Online Time Series Anomaly Detection with State Space Gaussian Processes

• URL: http://arxiv.org/abs/2201.06763v1
• Date: Tue, 18 Jan 2022 06:43:32 GMT
• Title: Online Time Series Anomaly Detection with State Space Gaussian Processes
• Authors: Christian Bock and Fran\c{c}ois-Xavier Aubet and Jan Gasthaus and Andrey Kan and Ming Chen and Laurent Callot
• Abstract summary: R-ssGPFA is an unsupervised online anomaly detection model for uni- and multivariate time series. For high-dimensional time series, we propose an extension of Gaussian process factor analysis to identify the common latent processes of the time series. Our model's robustness is improved by using a simple to skip Kalman updates when encountering anomalous observations.
• Score: 12.483273106706623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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