Poster
Precision and Recall for Time Series
Nesime Tatbul · Tae Jun Lee · Stan Zdonik · Mejbah Alam · Justin Gottschlich
Room 517 AB #116
Keywords: [ Activity and Event Recognition ] [ Benchmarks ] [ Classification ] [ Predictive Models ] [ Time Series Analysis ]
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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