Physics

Subsequence

Mathematics

Domain (mathematical analysis)

Margin (machine learning)

Data mining

Machine learning

Anomaly (physics)

Mathematical analysis

Artificial intelligence

Condensed matter physics

Computer science

Bounded function

Anomaly detection

Analytics

Proceedings of the VLDB Endowment

With the increasing demand for real-time analytics and decision making, anomaly detection methods need to operate over streams of values and handle drifts in data distribution. Unfortunately, existing approaches have severe limitations: they either require prior domain knowledge or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In addition, 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 problems, we propose SAND, a novel online method suitable for domain-agnostic anomaly detection. SAND aims to detect anomalies based on their distance to a model that represents normal behavior. SAND relies on a novel steaming methodology to incrementally update such model, which adapts to distribution drifts and omits obsolete data. The experimental results on several real-world datasets demonstrate that SAND correctly identifies single and recurrent anomalies without prior knowledge of the characteristics of these anomalies. SAND outperforms by a large margin the current state-of-the-art algorithms in terms of accuracy while achieving orders of magnitude speedups.

SAND