The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Récemment, des données de séries chronologiques multivariées ont été générées dans divers environnements, tels que les réseaux de capteurs et l'IoT, faisant de la détection des anomalies dans les données de séries chronologiques un sujet de recherche essentiel. Les détecteurs d'anomalies d'apprentissage non supervisé identifient les anomalies en entraînant un modèle sur des données normales et en produisant des résidus élevés pour les observations anormales. Cependant, un problème fondamental se pose car les anomalies n'entraînent pas systématiquement des résidus élevés, ce qui nécessite de se concentrer sur le modèles de séries chronologiques de résidus plutôt que des tailles résiduelles individuelles. Dans cet article, nous présentons un nouveau cadre comprenant deux détecteurs d'anomalies sérialisés: le premier modèle calcule les résidus comme d'habitude, tandis que le second évalue les modèle de série chronologique des résidus calculés pour déterminer s’ils sont normaux ou anormaux. Les expériences menées sur des données de séries chronologiques réelles démontrent l'efficacité du cadre proposé.
Byeongtae PARK
Hanyang University
Dong-Kyu CHAE
Hanyang University
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Byeongtae PARK, Dong-Kyu CHAE, "A Novel Anomaly Detection Framework Based on Model Serialization" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 3, pp. 420-423, March 2024, doi: 10.1587/transinf.2023EDL8024.
Abstract: Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8024/_p
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@ARTICLE{e107-d_3_420,
author={Byeongtae PARK, Dong-Kyu CHAE, },
journal={IEICE TRANSACTIONS on Information},
title={A Novel Anomaly Detection Framework Based on Model Serialization},
year={2024},
volume={E107-D},
number={3},
pages={420-423},
abstract={Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.},
keywords={},
doi={10.1587/transinf.2023EDL8024},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - A Novel Anomaly Detection Framework Based on Model Serialization
T2 - IEICE TRANSACTIONS on Information
SP - 420
EP - 423
AU - Byeongtae PARK
AU - Dong-Kyu CHAE
PY - 2024
DO - 10.1587/transinf.2023EDL8024
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2024
AB - Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.
ER -