research-article

Hybrid Deep Learning Model for Time Series Anomaly Detection

Authors:

Keon Myung LeeAuthors Info & Claims

RACS '23: Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems

Article No.: 30, Pages 1 - 5

https://doi.org/10.1145/3599957.3606232

Published: 29 August 2023 Publication History

Abstract

Multivariate time series anomaly detection is a fundamental challenge in real-world applications such as industry and business1. To address this issue, numerous models with diverse structures have been proposed. Each model leverages its unique structural characteristics to extract crucial features for time series anomaly detection. Our objective is to assess whether the performance of multivariate time series anomaly detection can be improved by employing a combination of models with different structures. In this paper, we propose a hybrid model for multivariate time series anomaly detection. The proposed hybrid model comprises two sub-models, each with a unique structure, and a simple layer. Each sub-model is designed to extract significant features from input time series. The simple layer combines the extracted features from both sub-models to generate the final output. Experimental results demonstrate that the proposed hybrid model outperforms single models.

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Cited By

Lee JYu SKim DChoi K(2024)Multi-Patching: Life-Log Classification with the Reconstructed Representation of Multivariate Time Series2024 15th International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC62082.2024.10827646(798-803)Online publication date: 16-Oct-2024
https://doi.org/10.1109/ICTC62082.2024.10827646
Lakey DSchlippe T(2024)A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection2024 IEEE Aerospace Conference10.1109/AERO58975.2024.10521015(1-11)Online publication date: 2-Mar-2024
https://doi.org/10.1109/AERO58975.2024.10521015
Rahman MTaheri HDababneh FKarganroudi SArhamnamazi S(2024)A review of distributed acoustic sensing applications for railroad condition monitoringMechanical Systems and Signal Processing10.1016/j.ymssp.2023.110983208(110983)Online publication date: Feb-2024
https://doi.org/10.1016/j.ymssp.2023.110983

Index Terms

Hybrid Deep Learning Model for Time Series Anomaly Detection
1. Applied computing
  1. Operations research
    1. Industry and manufacturing
2. Computing methodologies
  1. Artificial intelligence

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cover image ACM Conferences

RACS '23: Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems

August 2023

251 pages

ISBN:9798400702280

DOI:10.1145/3599957

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 29 August 2023

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RACS '23: International Conference on Research in Adaptive and Convergent Systems

August 6 - 10, 2023

Gdansk, Poland

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Cited By

Lee JYu SKim DChoi K(2024)Multi-Patching: Life-Log Classification with the Reconstructed Representation of Multivariate Time Series2024 15th International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC62082.2024.10827646(798-803)Online publication date: 16-Oct-2024
https://doi.org/10.1109/ICTC62082.2024.10827646
Lakey DSchlippe T(2024)A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection2024 IEEE Aerospace Conference10.1109/AERO58975.2024.10521015(1-11)Online publication date: 2-Mar-2024
https://doi.org/10.1109/AERO58975.2024.10521015
Rahman MTaheri HDababneh FKarganroudi SArhamnamazi S(2024)A review of distributed acoustic sensing applications for railroad condition monitoringMechanical Systems and Signal Processing10.1016/j.ymssp.2023.110983208(110983)Online publication date: Feb-2024
https://doi.org/10.1016/j.ymssp.2023.110983

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