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.
Cite
Text
Tatbul et al. "Precision and Recall for Time Series." Neural Information Processing Systems, 2018.Markdown
[Tatbul et al. "Precision and Recall for Time Series." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/tatbul2018neurips-precision/)BibTeX
@inproceedings{tatbul2018neurips-precision,
title = {{Precision and Recall for Time Series}},
author = {Tatbul, Nesime and Lee, Tae Jun and Zdonik, Stan and Alam, Mejbah and Gottschlich, Justin},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {1920-1930},
url = {https://mlanthology.org/neurips/2018/tatbul2018neurips-precision/}
}