Anomaly Detection for High-Dimensional Data Using Large Deviations Principle

Sreelekha Guggilam; Varun Chandola; Abani Patra

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arXiv · 2109.13698

Anomaly Detection for High-Dimensional Data Using Large Deviations Principle

Sreelekha Guggilam·Varun Chandola·Abani Patra

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Abstract

Most current anomaly detection methods suffer from the curse of dimensionality when dealing with high-dimensional data. We propose an anomaly detection algorithm that can scale to high-dimensional data using concepts from the theory of large deviations. The proposed Large Deviations Anomaly Detection (LAD) algorithm is shown to outperform state of art anomaly detection methods on a variety of large and high-dimensional benchmark data sets. Exploiting the ability of the algorithm to scale to high-dimensional data, we propose an online anomaly detection method to identify anomalies in a collection of multivariate time series. We demonstrate the applicability of the online algorithm in identifying counties in the United States with anomalous trends in terms of COVID-19 related cases and deaths. Several of the identified anomalous counties correlate with counties with documented poor response to the COVID pandemic.

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Sreelekha Guggilam, Varun Chandola, Abani Patra. 2021-09-28. Anomaly Detection for High-Dimensional Data Using Large Deviations Principle. https://arxiv.org/abs/2109.13698

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