9,236 research outputs found
ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN
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
MIM-GAN-based Anomaly Detection for Multivariate Time Series Data
The loss function of Generative adversarial network(GAN) is an important
factor that affects the quality and diversity of the generated samples for
anomaly detection. In this paper, we propose an unsupervised multiple time
series anomaly detection algorithm based on the GAN with message importance
measure(MIM-GAN). In particular, the time series data is divided into
subsequences using a sliding window. Then a generator and a discriminator
designed based on the Long Short-Term Memory (LSTM) are employed to capture the
temporal correlations of the time series data. To avoid the local optimal
solution of loss function and the model collapse, we introduce an exponential
information measure into the loss function of GAN. Additionally, a discriminant
reconstruction score consisting on discrimination and reconstruction loss is
taken into account. The global optimal solution for the loss function is
derived and the model collapse is proved to be avoided in our proposed
MIM-GAN-based anomaly detection algorithm. Experimental results show that the
proposed MIM-GAN-based anomaly detection algorithm has superior performance in
terms of precision, recall, and F1 score.Comment: 7 pages,6 figure
GANs para detecção de anomalias em séries temporais : um estudo de caso
O problema geral da detecção de anomalias se manifesta em diversos campos e se relaciona intimamente com inúmeros problemas específicos. A formulação habitual totalmente não supervisionada gera dificuldades adicionais na obtenção de representações relevantes para o problema, e restringe os métodos aplicáveis. Nesse contexto, o grande sucesso recente de soluções baseadas em GANs na modelagem de distribuições e processos arbitrários a partir de dados não supervisionados suscita grande interesse na sua aplicação ao problema de detecção de anomalias. Com objetivo de abordar esse tema, a aplicação de soluções baseadas em GANs para detecção de anomalias no contexto não supervisionado em séries temporais foi estudada. A partir de uma revisão da literatura dos princípios gerais de GANs e detecção de anomalias, trabalhos recentes aplicando GANs à séries temporais foram compilados e apresentados. Em sequência, um método específico, TadGan (GEIGER et al., 2020), foi selecionado para experimentação e estudos aprofundados sob o formato de estudo de caso. Uma implementação foi obtida e verificada, e uma metodologia para demonstrar o funcionamento e os princípios gerais do método e da aplicação de GANs às séries temporais sobre dados sintetizados a partir de funções analíticas desenvolvida e executada. Avaliou-se, em sequência, possíveis limitações do método, extraídas da literatura e propostas com base nos ensaios executados. Explorou-se a instabilidade do treinamento, e os possíveis impactos da entropia e características do processo de interesse na capacidade de detecção de anomalias. Sinais foram então sintetizados com a adição de tipos específicos de anomalias, a fim de verificar a generalidade do método quanto à natureza das anomalias, e uma coleção de sinais reais de domínios diversos compilados do conjunto UCR Anomaly Benchmark, de maneira a serem aplicados ao método. Por fim, alterações no método foram propostas, com maneiras alternativas de quantificar a anormalidade a partir dos modelos obtidos, e brevemente avaliadas. Os resultados obtidos permitiram a verificação e corroboração da grande aplicabilidade de GANs para detecção de anomalias em séries temporais, bem como da utilidade de experimentação com dados sintéticos analíticos para desenvolvimento de compreensão e validação de modificações. A exploração das limitações efetuadas permitiu o desenvolvimento de intuições sobre seus impactos no método, e sugeriram a possibilidade de influência de características do processo alvo na performance, e as modificações propostas apresentaram potencial de ganhos de performance, e apontaram a necessidade de estudos futuros aprofundados para a investigação posterior.The general problem of unsupervised anomaly detection in time series has applications in several different fields and is related to many specific problems. In the context of time series data, however, expert knowledge in the target application is often required in order to extract meaningful features of the process, which can be expansive and at times not possible. The field of Deep Learning provided techniques to tackle such problems with the possibility of automatic features extractions techniques, and present great potential in time series anomaly detection. The need for labeled data, however, restricts the direct application of several methods. GAN-based solutions have recently presented great performance in modeling arbitrary data distribution in unsupervised problems, showing a considerable conceptual potential in anomaly detection. In that context, with the goal of exploring the potential and applicability of GAN-based solutions for time series anomaly detection, the literature was reviewed for GAN and anomaly detection principles, and recent works specifically on GAN-based methods for time series anomaly detection summarized and presented. In sequence, a method was selected, TadGan (GEIGER et al., 2020), due to the presence of the main principles of GAN application to anomaly detection and its good reported performance in public benchmarks, for detailed investigation and exploration. An implementation of the method was obtained, and verified over a partial reproduction of the original article results. A series of experiments over synthetic generated data from analytical functions were then proposed and executed in order to verify the method’s principles in a controlled environment, as well as to raise intuitions of possible limitations. Limitations raised by the literature were then explored, and a new limitation, based on the influence of the signal entropy in the method performance, was informally formulated and investigated. Time series containing different types of anomalies were then synthesized, in order to verify the generality with respect to the nature of the anomalies, and data from real applications compiled from the UCR Anomaly Benchmark, and applied to the method. Finally, some modifications and suggestions of new scores derived from the method were presented, implemented and superficially analyzed. The results allowed to verify the great potential of the application of GAN-based techniques for unsupervised anomaly detection, as well as the benefits from exploring the method in synthetic data. The experimentation showed evidence of the explored limitations, in particular the influence of the target process entropy, and the proposed metrics showed potential of improvements and the need for further investigations
Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring
This is the final version. Available on open access from Elsevier via the DOI in this recordContamination events in water distribution networks (WDNs) can have a huge impact on water supply and public health; increasingly, online water quality sensors are deployed for real-time detection of contamination events. Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection; however, accurate extraction of spatial features in water quality signals remains challenging. This study proposed a contamination detection method based on generative adversarial networks (GANs). The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators. The model consists of two networks—a generator and a discriminator—the outputs of which are used to measure the degree of abnormality of water quality data at each time step, referred to as the anomaly score. Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores. Alarms are then generated from the fusion of single-site and multi-site models. The proposed method was tested on a WDN for various contamination events with different characteristics. Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes. Additionally, the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters, and water quality data from different sensor stations, highlighting its robustness and potential for practical application to real-time contamination events.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesRoyal Societ
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series
This paper proposes a novel fault diagnosis approach based on generative
adversarial networks (GAN) for imbalanced industrial time series where normal
samples are much larger than failure cases. We combine a well-designed feature
extractor with GAN to help train the whole network. Aimed at obtaining data
distribution and hidden pattern in both original distinguishing features and
latent space, the encoder-decoder-encoder three-sub-network is employed in GAN,
based on Deep Convolution Generative Adversarial Networks (DCGAN) but without
Tanh activation layer and only trained on normal samples. In order to verify
the validity and feasibility of our approach, we test it on rolling bearing
data from Case Western Reserve University and further verify it on data
collected from our laboratory. The results show that our proposed approach can
achieve excellent performance in detecting faulty by outputting much larger
evaluation scores
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification
Generative Adversarial Networks (GANs) have been used in many different
applications to generate realistic synthetic data. We introduce a novel GAN
with Autoencoder (GAN-AE) architecture to generate synthetic samples for
variable length, multi-feature sequence datasets. In this model, we develop a
GAN architecture with an additional autoencoder component, where recurrent
neural networks (RNNs) are used for each component of the model in order to
generate synthetic data to improve classification accuracy for a highly
imbalanced medical device dataset. In addition to the medical device dataset,
we also evaluate the GAN-AE performance on two additional datasets and
demonstrate the application of GAN-AE to a sequence-to-sequence task where both
synthetic sequence inputs and sequence outputs must be generated. To evaluate
the quality of the synthetic data, we train encoder-decoder models both with
and without the synthetic data and compare the classification model
performance. We show that a model trained with GAN-AE generated synthetic data
outperforms models trained with synthetic data generated both with standard
oversampling techniques such as SMOTE and Autoencoders as well as with state of
the art GAN-based models
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