Precision and Recall for Time Series
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.PDF Abstract NeurIPS 2018 PDF NeurIPS 2018 Abstract
Results from the Paper
Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.
No methods listed for this paper. Add relevant methods here