T‐VAE: Transformer‐Based Variational AutoEncoder for Perceiving Anomalies in Multivariate Time Series Data
ABSTRACT Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In real‐world scenarios, the sequence information in multivariate time series data, which encompasses the temporal order and dependencies among...
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| Published in: | Expert systems Vol. 42; no. 7 |
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01.07.2025
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| Abstract | ABSTRACT
Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In real‐world scenarios, the sequence information in multivariate time series data, which encompasses the temporal order and dependencies among high‐dimensional samples and features, can be complex and nonlinear. Additionally, the time series data often exhibit high volatility and are interspersed with noise data. These factors make anomaly perception in multivariate time series challenging. Despite the recent development of deep learning methods, only a few are able to address all of these challenges. In this paper, we propose a Transformer‐based Variational AutoEncoder (T‐VAE) for anomaly perception in multivariate time series data. The T‐VAE consists of two sub‐networks, the Representation Network and the Memory Network, and achieves end‐to‐end jointly optimisation. The Representation Network leverages self‐attention mechanisms and residual network structures to capture sequence information and metaphorical patterns from multivariate time series data. The Memory Network employs a Variational AutoEncoder to learn the distribution of normal data. It employs Maximum Mean Discrepancy to approximate the distribution of high‐volatility and noisy data to the distribution of the normal data. We evaluate T‐VAE on five datasets, showing superior performance and validating its effectiveness and robustness through comprehensive ablation studies and sensitivity analyses. |
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| AbstractList | Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In real‐world scenarios, the sequence information in multivariate time series data, which encompasses the temporal order and dependencies among high‐dimensional samples and features, can be complex and nonlinear. Additionally, the time series data often exhibit high volatility and are interspersed with noise data. These factors make anomaly perception in multivariate time series challenging. Despite the recent development of deep learning methods, only a few are able to address all of these challenges. In this paper, we propose a Transformer‐based Variational AutoEncoder (T‐VAE) for anomaly perception in multivariate time series data. The T‐VAE consists of two sub‐networks, the Representation Network and the Memory Network, and achieves end‐to‐end jointly optimisation. The Representation Network leverages self‐attention mechanisms and residual network structures to capture sequence information and metaphorical patterns from multivariate time series data. The Memory Network employs a Variational AutoEncoder to learn the distribution of normal data. It employs Maximum Mean Discrepancy to approximate the distribution of high‐volatility and noisy data to the distribution of the normal data. We evaluate T‐VAE on five datasets, showing superior performance and validating its effectiveness and robustness through comprehensive ablation studies and sensitivity analyses. ABSTRACT Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In real‐world scenarios, the sequence information in multivariate time series data, which encompasses the temporal order and dependencies among high‐dimensional samples and features, can be complex and nonlinear. Additionally, the time series data often exhibit high volatility and are interspersed with noise data. These factors make anomaly perception in multivariate time series challenging. Despite the recent development of deep learning methods, only a few are able to address all of these challenges. In this paper, we propose a Transformer‐based Variational AutoEncoder (T‐VAE) for anomaly perception in multivariate time series data. The T‐VAE consists of two sub‐networks, the Representation Network and the Memory Network, and achieves end‐to‐end jointly optimisation. The Representation Network leverages self‐attention mechanisms and residual network structures to capture sequence information and metaphorical patterns from multivariate time series data. The Memory Network employs a Variational AutoEncoder to learn the distribution of normal data. It employs Maximum Mean Discrepancy to approximate the distribution of high‐volatility and noisy data to the distribution of the normal data. We evaluate T‐VAE on five datasets, showing superior performance and validating its effectiveness and robustness through comprehensive ablation studies and sensitivity analyses. |
| Author | Jing, Jiwu Long, Chun Li, Chang Kiat, Yeo Chai |
| Author_xml | – sequence: 1 givenname: Chang orcidid: 0009-0009-7695-7814 surname: Li fullname: Li, Chang organization: University of Chinese Academy of Sciences – sequence: 2 givenname: Yeo Chai surname: Kiat fullname: Kiat, Yeo Chai organization: Nanyang Technological University – sequence: 3 givenname: Jiwu surname: Jing fullname: Jing, Jiwu organization: University of Chinese Academy of Sciences – sequence: 4 givenname: Chun surname: Long fullname: Long, Chun email: anquanip@cnic.cn organization: Chinese Academy of Sciences |
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| Cites_doi | 10.14778/3425879.3425885 10.14778/3467861.3467863 10.1016/j.knosys.2020.106622 10.3390/app11073194 10.14778/3514061.3514067 10.1016/j.cose.2023.103094 10.1109/ICDE60146.2024.00050 10.1016/j.ijforecast.2019.07.001 10.14778/3430915.3430918 10.1109/TC.2023.3257518 10.1109/TKDE.2023.3270293 10.5220/0006639801080116 10.1111/iere.12494 10.1145/3292500.3330680 10.18653/v1/2024.findings-acl.590 10.1016/j.ins.2023.119400 10.1016/j.compchemeng.2023.108560 10.3390/jcp2040039 10.1109/ICDM58522.2023.00150 10.14778/3415478.3415514 10.24963/ijcai.2024/818 10.1109/CySWater.2016.7469060 10.1145/3580305.3599295 10.1016/j.cie.2020.106435 10.1609/aaai.v35i5.16523 10.1016/j.inffus.2023.102165 10.1016/j.knosys.2020.105659 10.1016/j.cose.2021.102554 10.1016/j.inffus.2023.101921 10.1007/s00521-024-09962-x 10.1145/3292500.3330672 10.1145/3055366.3055375 10.3390/app13053183 10.18653/v1/P19-1378 10.1145/3219819.3219845 10.1109/JSTSP.2012.2237381 10.1109/ACCESS.2023.3349132 10.1109/JIOT.2019.2958185 10.1609/aaai.v33i01.33011409 10.1109/ICDM50108.2020.00093 10.1109/CCST.2019.8888419 10.1007/s10664-024-10533-w |
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| References | 2019; 7 2023; 13 2023; 35 2019; 3 2023; 36 2024; 104 2019; 33 2020; 143 2023; 100 2023; 127 2020; 36 2020; 14 2020; 13 2011; 12 2024; 12 2024 2023; 646 2024; 36 2013; 7 2024; 182 2022; 113 2021; 14 2021; 35 2021; 11 2021; 212 2020; 195 2023 2022 2018; 1 2021 2020 2022; 35 2019 2018 2017 2016 2014 2022; 2 2021; 62 2023; 72 e_1_2_8_28_1 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_26_1 e_1_2_8_49_1 Zong B. (e_1_2_8_54_1) 2018 Pedregosa F. (e_1_2_8_33_1) 2011 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_45_1 e_1_2_8_41_1 e_1_2_8_17_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_15_1 e_1_2_8_38_1 Mao R. (e_1_2_8_29_1) 2024 e_1_2_8_32_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_51_1 e_1_2_8_30_1 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_48_1 e_1_2_8_2_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_42_1 e_1_2_8_23_1 e_1_2_8_44_1 Li S. (e_1_2_8_22_1) 2019; 3 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 Li L. (e_1_2_8_21_1) 2022; 35 Song J. (e_1_2_8_39_1) 2023; 36 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_12_1 e_1_2_8_52_1 e_1_2_8_50_1 |
| References_xml | – volume: 143 year: 2020 article-title: An Optimized Model Using LSTM Network for Demand Forecasting publication-title: Computers & Industrial Engineering – start-page: 1 year: 2019 end-page: 8 – volume: 36 start-page: 57947 year: 2023 end-page: 57963 article-title: Memto: Memory‐Guided Transformer for Multivariate Time Series Anomaly Detection publication-title: In Advances in Neural Information Processing Systems – volume: 35 start-page: 6058 issue: 6 year: 2022 end-page: 6072 article-title: Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time‐Series publication-title: IEEE Transactions on Knowledge and Data Engineering – start-page: 31 year: 2016 end-page: 36 – volume: 11 start-page: 3194 issue: 7 year: 2021 article-title: Online Forecasting and Anomaly Detection Based on the Arima Model publication-title: Applied Sciences – volume: 14 start-page: 141 issue: 2 year: 2020 end-page: 153 article-title: Real‐Time Distance‐Based Outlier Detection in Data Streams publication-title: Proceedings of the VLDB Endowment – volume: 13 start-page: 2941 issue: 12 year: 2020 end-page: 2944 article-title: Graphan: Graph‐Based Subsequence Anomaly Detection publication-title: Proceedings of the VLDB Endowment – volume: 127 year: 2023 article-title: Gru‐Based Interpretable Multivariate Time Series Anomaly Detection in Industrial Control System publication-title: Computers & Security – volume: 12 start-page: 3768 year: 2024 end-page: 3789 article-title: A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions publication-title: IEEE Access – year: 2021 – year: 2024 – volume: 1 start-page: 108 year: 2018 end-page: 116 – volume: 2 start-page: 764 issue: 4 year: 2022 end-page: 777 article-title: Detection of SQL Injection Attack Using Machine Learning Techniques: A Systematic Literature Review publication-title: Journal of Cybersecurity and Privacy – volume: 7 start-page: 38 issue: 1 year: 2013 end-page: 49 article-title: Space‐Time Signal Processing for Distributed Pattern Detection in Sensor Networks publication-title: IEEE Journal of Selected Topics in Signal Processing – volume: 12 year: 2011 – volume: 100 year: 2023 article-title: KnowleNet: Knowledge Fusion Network for Multimodal Sarcasm Detection publication-title: Information Fusion – year: 2018 – year: 2014 – volume: 33 start-page: 1409 year: 2019 end-page: 1416 – volume: 7 start-page: 6481 issue: 7 year: 2019 end-page: 6494 article-title: Anomaly Detection for Iot Time‐Series Data: A Survey publication-title: IEEE Internet of Things Journal – start-page: 3888 year: 2019 end-page: 3898 – start-page: 841 year: 2020 end-page: 850 – volume: 113 year: 2022 article-title: Black‐Box Adversarial Attacks on Xss Attack Detection Model publication-title: Computers & Security – start-page: 387 year: 2018 end-page: 395 – volume: 104 year: 2024 article-title: Quantitative Stock Portfolio Optimization by Multi‐Task Learning Risk and Return publication-title: Information Fusion – volume: 35 start-page: 12591 issue: 12 year: 2023 end-page: 12604 article-title: Deep Isolation Forest for Anomaly Detection publication-title: IEEE Transactions on Knowledge and Data Engineering – volume: 13 start-page: 3183 issue: 5 year: 2023 article-title: Machine Learning Techniques to Detect a DDOS Attack in SDN: A Systematic Review publication-title: Applied Sciences – volume: 35 start-page: 4027 year: 2021 end-page: 4035 – volume: 195 year: 2020 article-title: K‐Means‐Based Isolation Forest publication-title: Knowledge‐Based Systems – volume: 646 year: 2023 article-title: Fuzzy Granular Anomaly Detection Using Markov Random Walk publication-title: Information Sciences – volume: 62 start-page: 469 issue: 2 year: 2021 end-page: 520 article-title: Business Cycles, Trend Elimination, and the Hp Filter publication-title: International Economic Review – start-page: 3009 year: 2019 end-page: 3017 – volume: 182 year: 2024 article-title: Time Series Forecasting and Anomaly Detection Using Deep Learning publication-title: Computers & Chemical Engineering – start-page: 2828 year: 2019 end-page: 2837 – year: 2022 – year: 2020 – volume: 14 start-page: 1717 issue: 10 year: 2021 end-page: 1729 article-title: Sand: Streaming Subsequence Anomaly Detection publication-title: Proceedings of the VLDB Endowment – year: 2023 – volume: 72 start-page: 2656 issue: 9 year: 2023 end-page: 2667 article-title: Improving Log‐Based Anomaly Detection by Pre‐Training Hierarchical Transformers publication-title: IEEE Transactions on Computers – volume: 3 start-page: 32 year: 2019 article-title: Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting publication-title: Advances in Neural Information Processing Systems – start-page: 1211 year: 2023 end-page: 1216 – volume: 36 start-page: 1181 issue: 3 year: 2020 end-page: 1191 article-title: Deepar: Probabilistic Forecasting With Autoregressive Recurrent Networks publication-title: International Journal of Forecasting – start-page: 25 year: 2017 end-page: 28 – volume: 212 year: 2021 article-title: Automl: A Survey of the State‐of‐the‐Art publication-title: Knowledge‐Based Systems – year: 2019 – volume: 36 start-page: 16213 issue: 26 year: 2024 end-page: 16231 article-title: Corrector LSTM: Built‐In Training Data Correction for Improved Time‐Series Forecasting publication-title: Neural Computing and Applications – ident: e_1_2_8_45_1 – ident: e_1_2_8_42_1 doi: 10.14778/3425879.3425885 – ident: e_1_2_8_8_1 doi: 10.14778/3467861.3467863 – ident: e_1_2_8_12_1 doi: 10.1016/j.knosys.2020.106622 – ident: e_1_2_8_20_1 doi: 10.3390/app11073194 – ident: e_1_2_8_43_1 doi: 10.14778/3514061.3514067 – ident: e_1_2_8_41_1 doi: 10.1016/j.cose.2023.103094 – volume: 36 start-page: 57947 year: 2023 ident: e_1_2_8_39_1 article-title: Memto: Memory‐Guided Transformer for Multivariate Time Series Anomaly Detection publication-title: In Advances in Neural Information Processing Systems – ident: e_1_2_8_11_1 doi: 10.1109/ICDE60146.2024.00050 – volume-title: Proceedings of the Annual Meeting of the Cognitive Science Society (Cogsci) year: 2024 ident: e_1_2_8_29_1 – ident: e_1_2_8_36_1 doi: 10.1016/j.ijforecast.2019.07.001 – ident: e_1_2_8_19_1 doi: 10.14778/3430915.3430918 – ident: e_1_2_8_32_1 – ident: e_1_2_8_13_1 doi: 10.1109/TC.2023.3257518 – ident: e_1_2_8_47_1 doi: 10.1109/TKDE.2023.3270293 – ident: e_1_2_8_37_1 doi: 10.5220/0006639801080116 – volume-title: Scikit‐Learn: Machine Learning in Python year: 2011 ident: e_1_2_8_33_1 – ident: e_1_2_8_34_1 doi: 10.1111/iere.12494 – ident: e_1_2_8_35_1 doi: 10.1145/3292500.3330680 – ident: e_1_2_8_18_1 – ident: e_1_2_8_26_1 doi: 10.18653/v1/2024.findings-acl.590 – ident: e_1_2_8_23_1 doi: 10.1016/j.ins.2023.119400 – ident: e_1_2_8_15_1 doi: 10.1016/j.compchemeng.2023.108560 – ident: e_1_2_8_4_1 doi: 10.3390/jcp2040039 – ident: e_1_2_8_25_1 doi: 10.1109/ICDM58522.2023.00150 – ident: e_1_2_8_7_1 doi: 10.14778/3415478.3415514 – ident: e_1_2_8_28_1 doi: 10.24963/ijcai.2024/818 – ident: e_1_2_8_30_1 doi: 10.1109/CySWater.2016.7469060 – ident: e_1_2_8_50_1 doi: 10.1145/3580305.3599295 – ident: e_1_2_8_2_1 doi: 10.1016/j.cie.2020.106435 – ident: e_1_2_8_10_1 doi: 10.1609/aaai.v35i5.16523 – volume: 3 start-page: 32 year: 2019 ident: e_1_2_8_22_1 article-title: Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting publication-title: Advances in Neural Information Processing Systems – volume-title: International Conference on Learning Representations year: 2018 ident: e_1_2_8_54_1 – ident: e_1_2_8_24_1 doi: 10.1016/j.inffus.2023.102165 – ident: e_1_2_8_16_1 doi: 10.1016/j.knosys.2020.105659 – ident: e_1_2_8_44_1 doi: 10.1016/j.cose.2021.102554 – ident: e_1_2_8_51_1 doi: 10.1016/j.inffus.2023.101921 – ident: e_1_2_8_6_1 doi: 10.1007/s00521-024-09962-x – ident: e_1_2_8_40_1 doi: 10.1145/3292500.3330672 – ident: e_1_2_8_3_1 doi: 10.1145/3055366.3055375 – ident: e_1_2_8_46_1 – ident: e_1_2_8_5_1 doi: 10.3390/app13053183 – ident: e_1_2_8_27_1 doi: 10.18653/v1/P19-1378 – ident: e_1_2_8_14_1 doi: 10.1145/3219819.3219845 – ident: e_1_2_8_31_1 doi: 10.1109/JSTSP.2012.2237381 – ident: e_1_2_8_48_1 doi: 10.1109/ACCESS.2023.3349132 – ident: e_1_2_8_9_1 doi: 10.1109/JIOT.2019.2958185 – ident: e_1_2_8_52_1 doi: 10.1609/aaai.v33i01.33011409 – ident: e_1_2_8_49_1 – ident: e_1_2_8_53_1 doi: 10.1109/ICDM50108.2020.00093 – ident: e_1_2_8_38_1 doi: 10.1109/CCST.2019.8888419 – ident: e_1_2_8_17_1 doi: 10.1007/s10664-024-10533-w – volume: 35 start-page: 6058 issue: 6 year: 2022 ident: e_1_2_8_21_1 article-title: Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time‐Series publication-title: IEEE Transactions on Knowledge and Data Engineering |
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Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In... Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In... |
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| SubjectTerms | Ablation anomaly perception Multivariate analysis Perception Representations Sensitivity analysis Time series time series data transformer variational auto‐encoder Volatility |
| Title | T‐VAE: Transformer‐Based Variational AutoEncoder for Perceiving Anomalies in Multivariate Time Series Data |
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