NeurIPS 2018spotlight264 citations
Precision and Recall for Time Series
Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich
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.
BibTeX
@inproceedings{NEURIPS2018_8f468c87,
author = {Tatbul, Nesime and Lee, Tae Jun and Zdonik, Stan and Alam, Mejbah and Gottschlich, Justin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Precision and Recall for Time Series},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/8f468c873a32bb0619eaeb2050ba45d1-Paper.pdf},
volume = {31},
year = {2018}
}