← Back to papers
2018 NIPS NeurIPS 2018

Precision and Recall for Time Series

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.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
📈 Trend Setter — Anomaly Detection
🧭 Keyword Pioneer — time series anomaly detection
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Nesime Tatbul , Tae Jun Lee , Stan Zdonik , Mejbah Alam , Justin Gottschlich

Topics

Data Science & Analytics > Methods > Time Series Data Science & Analytics > Methods > Time Series Analysis Machine Learning > Optimization & Theory > Evaluation Machine Learning > Learning Types > Anomaly Detection

Keywords

anomaly detection time series anomaly detection temporal classification time series classification recall metric precision metric range-based anomaly
Download PDF

Related papers

Maximum Causal Tsallis Entropy Imitation Learning 2018
Recurrent World Models Facilitate Policy Evolution 2018
Bandit Learning in Concave N-Person Games 2018
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation 2018
PAC-Bayes bounds for stable algorithms with instance-dependent priors 2018