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arXiv · 1803.03639

Precision and Recall for Time Series

Nesime Tatbul·Tae Jun Lee·Stan Zdonik·Mejbah Alam·Justin Gottschlich
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Abstract

Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-based, meaning they occur over a period of time. Motivated by this observation, we present a new mathematical model to evaluate the accuracy of time series classification algorithms. Our model expands the well-known Precision and Recall metrics to measure ranges, while simultaneously enabling customization support for domain-specific preferences.

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Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich. 2018-03-08. Precision and Recall for Time Series. https://arxiv.org/abs/1803.03639

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