# Precision and Recall for Time Series

arXiv: https://arxiv.org/abs/1803.03639 · HTML: https://mlsanity.com/paper/1803.03639/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 30 of 67)

- [CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching](https://mlsanity.com/paper/2410.12261/) (2024)
- [Local Evaluation of Time Series Anomaly Detection Algorithms](https://mlsanity.com/paper/2206.13167/) (2022)
- [TAB: Unified Benchmarking of Time Series Anomaly Detection Methods](https://mlsanity.com/paper/2506.18046/) (2025)
- [Neural Contextual Anomaly Detection for Time Series](https://mlsanity.com/paper/2107.07702/) (2021)
- [Large language models can be zero-shot anomaly detectors for time series?](https://mlsanity.com/paper/2405.14755/) (2024)
- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models](https://mlsanity.com/paper/2402.10802/) (2024)
- [Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?](https://mlsanity.com/paper/2405.02678/) (2024)
- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [PATE: Proximity-Aware Time series anomaly Evaluation](https://mlsanity.com/paper/2405.12096/) (2024)
- [VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection](https://mlsanity.com/paper/2502.13318/) (2025)
- [Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies](https://mlsanity.com/paper/2505.20765/) (2025)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter](https://mlsanity.com/paper/2510.16014/) (2025)
- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2408.03747/) (2024)
- [Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems](https://mlsanity.com/paper/2104.02545/) (2021)
- [Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms](https://mlsanity.com/paper/2105.12818/) (2021)
- [A Meta-level Analysis of Online Anomaly Detectors](https://mlsanity.com/paper/2209.05899/) (2022)
- [Range-Based Equal Error Rate for Spoof Localization](https://mlsanity.com/paper/2305.17739/) (2023)
- [Probably Approximately Precision and Recall Learning](https://mlsanity.com/paper/2411.13029/) (2024)
- [Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series](https://mlsanity.com/paper/2203.07861/) (2022)
- [Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2510.16511/) (2025)
- [Matrix Profile for Anomaly Detection on Multidimensional Time Series](https://mlsanity.com/paper/2409.09298/) (2024)
- [M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding](https://mlsanity.com/paper/2504.15225/) (2025)
- [Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction](https://mlsanity.com/paper/2603.21612/) (2026)
- [SoftED: Metrics for Soft Evaluation of Time Series Event Detection](https://mlsanity.com/paper/2304.00439/) (2023)
- [A Survey of Time Series Anomaly Detection Methods in the AIOps Domain](https://mlsanity.com/paper/2308.00393/) (2023)
- [A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection](https://mlsanity.com/paper/2403.12864/) (2024)
- [Towards Unbiased Evaluation of Time-series Anomaly Detector](https://mlsanity.com/paper/2409.13053/) (2024)

## Abstract (arXiv)

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.