Precision and Recall for Time Series
arXiv:1803.03639
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.
11 pages, 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada
Cited by in corpus (5)
- Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms
- Master your Metrics with Calibration
- Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
- Statistical Evaluation of Anomaly Detectors for Sequences
- Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems