BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data

In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ma, Mingrui [verfasserIn] Han, Lansheng [verfasserIn] Zhou, Chunjie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multivariate time series data Bi-Transformer model Model-agnostic meta learning Adaptive multi-head attention mechanism Self-conditioning mechanism

Übergeordnetes Werk:	Enthalten in: Advanced engineering informatics - Amsterdam [u.a.] : Elsevier Science, 2002, 56
Übergeordnetes Werk:	volume:56

DOI / URN:	10.1016/j.aei.2023.101949

Katalog-ID:	ELV01013199X

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520			\|a In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas.
650		4	\|a Multivariate time series data
650		4	\|a Bi-Transformer model
650		4	\|a Model-agnostic meta learning
650		4	\|a Adaptive multi-head attention mechanism
650		4	\|a Self-conditioning mechanism
700	1		\|a Han, Lansheng \|e verfasserin \|4 aut
700	1		\|a Zhou, Chunjie \|e verfasserin \|4 aut
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author_variant	m m mm l h lh c z cz
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publishDate	2023
allfields	10.1016/j.aei.2023.101949 doi (DE-627)ELV01013199X (ELSEVIER)S1474-0346(23)00077-0 DE-627 ger DE-627 rda eng 004 620 670 VZ 54.72 bkl 50.03 bkl Ma, Mingrui verfasserin (orcid)0000-0002-2073-6424 aut BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas. Multivariate time series data Bi-Transformer model Model-agnostic meta learning Adaptive multi-head attention mechanism Self-conditioning mechanism Han, Lansheng verfasserin aut Zhou, Chunjie verfasserin aut Enthalten in Advanced engineering informatics Amsterdam [u.a.] : Elsevier Science, 2002 56 Online-Ressource (DE-627)320423565 (DE-600)2002862-3 (DE-576)094478821 1474-0346 nnns volume:56 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 56
spelling	10.1016/j.aei.2023.101949 doi (DE-627)ELV01013199X (ELSEVIER)S1474-0346(23)00077-0 DE-627 ger DE-627 rda eng 004 620 670 VZ 54.72 bkl 50.03 bkl Ma, Mingrui verfasserin (orcid)0000-0002-2073-6424 aut BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas. Multivariate time series data Bi-Transformer model Model-agnostic meta learning Adaptive multi-head attention mechanism Self-conditioning mechanism Han, Lansheng verfasserin aut Zhou, Chunjie verfasserin aut Enthalten in Advanced engineering informatics Amsterdam [u.a.] : Elsevier Science, 2002 56 Online-Ressource (DE-627)320423565 (DE-600)2002862-3 (DE-576)094478821 1474-0346 nnns volume:56 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 56
allfields_unstemmed	10.1016/j.aei.2023.101949 doi (DE-627)ELV01013199X (ELSEVIER)S1474-0346(23)00077-0 DE-627 ger DE-627 rda eng 004 620 670 VZ 54.72 bkl 50.03 bkl Ma, Mingrui verfasserin (orcid)0000-0002-2073-6424 aut BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas. Multivariate time series data Bi-Transformer model Model-agnostic meta learning Adaptive multi-head attention mechanism Self-conditioning mechanism Han, Lansheng verfasserin aut Zhou, Chunjie verfasserin aut Enthalten in Advanced engineering informatics Amsterdam [u.a.] : Elsevier Science, 2002 56 Online-Ressource (DE-627)320423565 (DE-600)2002862-3 (DE-576)094478821 1474-0346 nnns volume:56 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 56
allfieldsGer	10.1016/j.aei.2023.101949 doi (DE-627)ELV01013199X (ELSEVIER)S1474-0346(23)00077-0 DE-627 ger DE-627 rda eng 004 620 670 VZ 54.72 bkl 50.03 bkl Ma, Mingrui verfasserin (orcid)0000-0002-2073-6424 aut BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas. Multivariate time series data Bi-Transformer model Model-agnostic meta learning Adaptive multi-head attention mechanism Self-conditioning mechanism Han, Lansheng verfasserin aut Zhou, Chunjie verfasserin aut Enthalten in Advanced engineering informatics Amsterdam [u.a.] : Elsevier Science, 2002 56 Online-Ressource (DE-627)320423565 (DE-600)2002862-3 (DE-576)094478821 1474-0346 nnns volume:56 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 56
allfieldsSound	10.1016/j.aei.2023.101949 doi (DE-627)ELV01013199X (ELSEVIER)S1474-0346(23)00077-0 DE-627 ger DE-627 rda eng 004 620 670 VZ 54.72 bkl 50.03 bkl Ma, Mingrui verfasserin (orcid)0000-0002-2073-6424 aut BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas. Multivariate time series data Bi-Transformer model Model-agnostic meta learning Adaptive multi-head attention mechanism Self-conditioning mechanism Han, Lansheng verfasserin aut Zhou, Chunjie verfasserin aut Enthalten in Advanced engineering informatics Amsterdam [u.a.] : Elsevier Science, 2002 56 Online-Ressource (DE-627)320423565 (DE-600)2002862-3 (DE-576)094478821 1474-0346 nnns volume:56 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 50.03 Methoden und Techniken der Ingenieurwissenschaften VZ AR 56
language	English
source	Enthalten in Advanced engineering informatics 56 volume:56
sourceStr	Enthalten in Advanced engineering informatics 56 volume:56
format_phy_str_mv	Article
bklname	Künstliche Intelligenz Methoden und Techniken der Ingenieurwissenschaften
institution	findex.gbv.de
topic_facet	Multivariate time series data Bi-Transformer model Model-agnostic meta learning Adaptive multi-head attention mechanism Self-conditioning mechanism
dewey-raw	004
isfreeaccess_bool	false
container_title	Advanced engineering informatics
authorswithroles_txt_mv	Ma, Mingrui @@aut@@ Han, Lansheng @@aut@@ Zhou, Chunjie @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320423565
dewey-sort	14
id	ELV01013199X
language_de	englisch
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author	Ma, Mingrui
spellingShingle	Ma, Mingrui ddc 004 bkl 54.72 bkl 50.03 misc Multivariate time series data misc Bi-Transformer model misc Model-agnostic meta learning misc Adaptive multi-head attention mechanism misc Self-conditioning mechanism BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
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topic_title	004 620 670 VZ 54.72 bkl 50.03 bkl BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data Multivariate time series data Bi-Transformer model Model-agnostic meta learning Adaptive multi-head attention mechanism Self-conditioning mechanism
topic	ddc 004 bkl 54.72 bkl 50.03 misc Multivariate time series data misc Bi-Transformer model misc Model-agnostic meta learning misc Adaptive multi-head attention mechanism misc Self-conditioning mechanism
topic_unstemmed	ddc 004 bkl 54.72 bkl 50.03 misc Multivariate time series data misc Bi-Transformer model misc Model-agnostic meta learning misc Adaptive multi-head attention mechanism misc Self-conditioning mechanism
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title	BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
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title_full	BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
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title_sort	btad: a binary transformer deep neural network model for anomaly detection in multivariate time series data
title_auth	BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
abstract	In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas.
abstractGer	In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas.
abstract_unstemmed	In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the A U C and F 1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas.
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title_short	BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
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