RT Journal Article
T1 A framework for adapting online prediction algorithms to outlier detection over time series
A1 Iturria, Alaiñe
A1 Labaien, Jokin
A1 Charramendieta, Santi
A1 Lojo, Aizea
A1 Del Ser, Javier
A1 Herrera, Francisco
AB This study introduces a novel framework that eases the adoption of any online prediction algorithm for outlier detection over time series data. The proposed framework comprises both streaming data normalization and online anomaly scoring and identification based on prediction errors. To demonstrate the utility of the proposed framework, a novel neural-network-based online time series anomaly detection algorithm called EORELM-AD is developed by implementing the steps of the proposed framework over an ensemble of online recurrent extreme learning machines. Extensive experiments on well-known benchmark datasets for time series outlier detection are presented and discussed, yielding two main conclusions. First, the performance of the proposed EORELM-AD detector is competitive in comparison to several state-of-the-art outlier detection algorithms. Second, the proposed framework is a useful tool for adapting an online time series prediction algorithm to outlier detection.
SN 0950-7051
YR 2022
FD 2022-11-28
LK https://hdl.handle.net/11556/3447
UL https://hdl.handle.net/11556/3447
LA eng
NO Iturria , A , Labaien , J , Charramendieta , S , Lojo , A , Del Ser , J & Herrera , F 2022 , ' A framework for adapting online prediction algorithms to outlier detection over time series ' , Knowledge-Based Systems , vol. 256 , 109823 . https://doi.org/10.1016/j.knosys.2022.109823
NO Publisher Copyright: © 2022 Elsevier B.V.
NO The authors would like to thank the Basque Government for their financial support of this research through the ELKARTEK program under the 3KIA project (grant no. KK-2020/00049 ) and the DAEKIN project (grant no. KK-2020/00035 ). Javier Del Ser also acknowledges funding support from the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1456-22 ).
DS TECNALIA Publications
RD 28 jul 2024