TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data

Xu, Liyan; Xu, Kang; Qin, Yinchuan; Li, Yixuan; Huang, Xingting; Lin, Zhenyang; Ye, Ning; Ji, Xuechun

doi:10.3390/app12168085

Cited by 24 publications

(7 citation statements)

References 22 publications

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“…Combined with the distributed optical fiber sensing system, the algorithm can be applied in other fields, such as detecting train wheel abnormalities, oil pipeline safety monitoring, aircraft runway safety warning, and road vehicle positioning. In the future, as more vibration signal data are collected, combined with the improvement method of the Transformer model 32,33 and the views of other fields, [34][35][36][37][38][39] the universality and generalization ability of this track bed defect classification model will be improved.…”

Section: Discussionmentioning

confidence: 99%

Invisible track bed defect classification method based on distributed optical fiber sensing system and FFT Attention Transformer model

Chen,

Lin,

et al. 2024

Opt. Eng.

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An invisible track bed defect classification method based on distributed optical fiber sensing data acquisition and an attention Transformer model mechanism under the frequency domain is proposed. The vibration sensing data contain structural safety information of vehicles, rails, track beds, etc., covering the entire time period and entire track area of subway operation. To classify the invisible track bed defect rapidly and accurately, the original vibration signals are first reduced by downsampling and envelope signal extraction. According to the regular characteristics of different types of signals, an fast Fourier transform (FFT) Attention Transformer (FFT-Attn-Transformer) sequence feature extraction architecture with a high recognition accuracy is proposed for model training.The results demonstrate that the accuracy, precision, recall rate, and F 1-score are all above 98% using the proposed model, and the recognition accuracy of the defect test area is 99.47%, which has extremely high stability and accuracy, providing an innovative and feasible idea for the lack of effective monitoring scheme for invisible track bed defects.

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Section: Discussionmentioning

confidence: 99%

Invisible track bed defect classification method based on distributed optical fiber sensing system and FFT Attention Transformer model

Chen,

Lin,

et al. 2024

Opt. Eng.

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“…mechanisms. GTMs and LSTMs, given their capacity to handle sequences, can be employed in AML to detect adversarial attacks in time-series data [305], especially relevant for communications data in 6G networks. FGMs and DGMs can be applied for modeling the complex data distributions of regular traffic and identifying discrepancies in the form of adversarial attacks.…”

Section: Dgmsmentioning

confidence: 99%

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Celik,

Eltawil

2024

IEEE Open J. Commun. Soc.

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As we transition from the 5G epoch, a new horizon beckons with the advent of 6G, seeking a profound fusion with novel communication paradigms and emerging technological trends, bringing once-futuristic visions to life along with added technical intricacies. Although analytical models lay the foundations and offer systematic insights, we have recently witnessed a noticeable surge in research suggesting machine learning (ML) and artificial intelligence (AI) can efficiently deal with complex problems by complementing or replacing model-based approaches. The majority of data-driven wireless research leans heavily on discriminative AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. With these appealing attributes, GenAI can replace or supplement DAI methods in various capacities.Accordingly, this combined tutorial-survey paper commences with preliminaries of 6G and wireless intelligence by outlining candidate 6G applications and services, presenting a taxonomy of state-of-theart DAI models, exemplifying prominent DAI use cases, and elucidating the multifaceted ways through which GenAI enhances DAI. Subsequently, we present a tutorial on GMs by spotlighting seminal examples such as generative adversarial networks, variational autoencoders, flow-based GMs, diffusion-based GMs, generative transformers, large language models, autoregressive GMs, to name a few. Contrary to the prevailing belief that GenAI is a nascent trend, our exhaustive review of approximately 120 technical papers demonstrates the scope of research across core wireless research areas, including 1) physical layer design; 2) network optimization, organization, and management; 3) network traffic analytics; 4) crosslayer network security; and 5) localization & positioning. Furthermore, we outline the central role of GMs in pioneering areas of 6G network research, including semantic communications, integrated sensing and communications, THz communications, extremely large antenna arrays, near-field communications, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the multifarious challenges ahead, suggesting potential strategies and promising remedies. Given its depth and breadth, we are confident that this tutorial-cum-survey will serve as a pivotal reference for researchers and professionals delving into this dynamic and promising domain.

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“…Recently, TGAN-AD, a transformer-based GAN [35] is proposed for anomaly detection. TGAN-AD uses a similar setting as that of RGAN.…”

Section: Related Workmentioning

confidence: 99%

ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN

Bashar,

Nayak

2023

Preprint

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Anomaly detection in time series data, to identify points that deviate from normal behaviour, is a common problem in various domains such as manufacturing, medical imaging, and cybersecurity. Recently, Generative Adversarial Networks (GANs) are shown to be effective in detecting anomalies in time series data. The neural network architecture of GANs (i.e. Generator and Discriminator) can significantly improve anomaly detection accuracy. In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both univariate and multivariate time series data in an unsupervised setting. We evaluate the performance of ALGAN on 46 real-world univariate time series datasets and a large multivariate dataset that spans multiple domains. Our experiments demonstrate that ALGAN outperforms traditional, neural network-based, and other GAN-based methods for anomaly detection in time series data.

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TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data

Cited by 24 publications

References 22 publications

Invisible track bed defect classification method based on distributed optical fiber sensing system and FFT Attention Transformer model

Invisible track bed defect classification method based on distributed optical fiber sensing system and FFT Attention Transformer model

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN

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