@misc{10481/84967,
year = {2023},
month = {8},
url = {https://hdl.handle.net/10481/84967},
abstract = {Anomaly detection aims to identify observations that differ significantly from the majority of the data. Time series, which are data with a temporal component, is often used for anomaly detection. Identifying anomalies is not perfect and may produce many false positives, which labels standard data as anomalous. In this context, false positive mitigation is the task of reducing the number of false positives tagged by the anomaly detector, and thus both problems are closely linked. Moreover, current techniques for false positive mitigation are ad-hoc solutions for specific data sets. In this paper, we propose a novel two-stage methodology for Multivariate Anomaly Detection for Time Series and False Positive Mitigation, namely 
 methodology, which creates the fusion of two learning models. The first stage is a multivariate anomaly detection stage. The second stage consists of training a new classifier on the false and true positives from the anomaly detector, which refines the observations labeled as anomalous by the anomaly detector to obtain more accurate and higher-quality results. Experiments using two benchmark data sets, as well as a real-world case study have shown the performance and validity of the proposal.},
organization = {Spanish Ministry of Science
and Innovation under project TED2021-132702B-C21 funded by
MCIN/AEI/10.13039/501100011033 ‘‘European Union PRTR},
organization = {Project PID2020-119478GBI00},
organization = {Contract UGR-AM OTRI-4260},
organization = {EQC2018-005084-P
project, granted by the Spain’s Ministry of Science and Innovation and
European Regional Development Fund (ERDF)},
organization = {the SOMM17/6110/
UGR project, granted by the Andalusian Consejería de Conocimiento,
Investigación  y Universidades},
organization = {European Regional Development
Funds (ERDF)},
organization = {Universidad de Granada
/ CBUA.},
publisher = {Elsevier},
keywords = {Anomaly detection},
keywords = {Outlier detection},
keywords = {False positive mitigation},
keywords = {Deep learning},
keywords = {Multivariate time series},
keywords = {Predictive maintenance},
title = {Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series},
doi = {10.1016/j.inffus.2023.101957},
author = {López García, David and Aguilera Martos, Ignacio and Herrera Triguero, Francisco and García Gil, Diego Jesús and Luengo Martín, Julián},
}