Online Time Series Anomaly Detection with State Space Gaussian Processes

TL;DR

This paper introduces r-ssGPFA, an unsupervised online anomaly detection method for time series that combines Gaussian processes with state space models, offering interpretability, efficiency, and robustness.

Contribution

The paper presents a novel online anomaly detection model using state space Gaussian processes with an extension for high-dimensional data and a heuristic for robustness.

Findings

Competitive performance on benchmark datasets

Reduced computational cost compared to state-of-the-art methods

Enhanced interpretability through latent process analysis

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.