SAND in Action: Subsequence Anomaly Detection for Streams

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Abstract

Subsequence anomaly detection in long data series is a significant problem. While the demand for real-time analytics and decision-making increases, anomaly detection methods have to operate over streams and handle drifts in data distribution. Nevertheless, existing approaches either require prior domain knowledge or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. Moreover, subsequence anomaly detection methods usually require access to the entire dataset and are not able to learn and detect anomalies in streaming settings. To address these limitations, we propose SAND, a novel online system suitable for domain-agnostic anomaly detection. SAND relies on a novel steaming methodology to incrementally update a model that adapts to distribution drifts and omits obsolete data. We demonstrate our system over different streaming scenarios and compare SAND with other subsequence anomaly detection methods.