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Fine-Grained multivariate time series Anomaly Detection in IoT

Computers, Materials & Continua 2023年第6期75卷 5027-5047页

作者： Shiming He Meng Guo Bo Yang Osama Alfarraj Amr Tolba Pradip Kumar Sharma Xi’ai Yan School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computing Science University of AberdeenAberdeenAB243FXUK Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police AcademyChangsha410138China

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)*** time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and ***,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is ***,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between ***,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)*** avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained *** on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal *** simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

关键词： multivariate time series graph attention neural network fine-grained anomaly detection

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A Total Variation Based Method for multivariate time series Segmentation

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Advances in Applied Mathematics and Mechanics 2023年第2期15卷 300-321页

作者： Min Li Yumei Huang Youwei Wen School of Mathematics and Statistics Center for Data ScienceLanzhou UniversityLanzhouGansu 730000China School of Mathematics and Statistics Hunan Normal UniversityChangshaHunan 410081China and Key Laboratory of Computing and Stochastic Mathematics(LCSM)Ministry of Education of China

multivariate time series segmentation is an important problem in data mining and it has arisen in more and more practical applications in recent *** task of time series segmentation is to partition a time series into segments by detecting the abrupt changes or anomalies in the time *** time series segmentation can provide meaningful information for further data analysis,prediction and policy decision.A time series can be considered as a piecewise continuous function,it is natural to take its total variation norm as a prior information of this time *** this paper,by minimizing the negative log-likelihood function of a time series,we propose a total variation based model for multivariate time series *** iterative process is applied to solve the proposed model and a search combined the dynamic programming method is designed to determine the *** experimental results show that the proposed method is efficient for multivariate time series segmentation and it is competitive to the existing methods for multivariate time series segmentation.

关键词： multivariate time series segmentation total variation dynamic programming

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A Memory-Guided Anomaly Detection Model with Contrastive Learning for multivariate time series

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Computers, Materials & Continua 2023年第11期77卷 1893-1910页

作者： Wei Zhang Ping He Ting Li Fan Yang Ying Liu School of Electronic and Information Engineering Hebei University of TechnologyTianjin300401China School of Artificial Intelligence Hebei University of TechnologyTianjin300401China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly *** limitations can result in the misjudgment of models,leading to a degradation in overall detection *** paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block(CLME)to overcome the above *** contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained *** memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration *** two modules together effectively alleviate the problem of ***,this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature *** a strategy can enlarge the discrepancies between normal and abnormal data,which is more conducive to anomaly *** proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly *** validate the efficacy of the proposed approach,extensive experiments are conducted on well-established benchmark datasets,including SWaT,PSM,WADI,and *** results demonstrate outstanding performance,with F1 scores of 90.58%,94.83%,91.58%,and 91.75%,*** findings affirm the superiority of the CLME model over existing stateof-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.

关键词： Anomaly detection multivariate time series contrastive learning memory network

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Exploring and visualizing temporal relations in multivariate time series

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Visual Informatics 2023年第4期7卷 57-72页

作者： Gota Shirato Natalia Andrienko Gennady Andrienko Fraunhofer IAIS Sankt AugustinGermany University of Bonn BonnGermany City University of LondonLondonUK

This paper introduces an approach to analyzing multivariate time series(MVTS)data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic *** paper focuses on two core challenges:identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute *** proposed approach combines existing methods for univariate pattern extraction,computation of temporal relations according to the Allen’s time interval algebra,visual displays of the temporal relations,and interactive query operations into a cohesive visual analytics *** paper describes the application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match,illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.

关键词： Temporal relations Temporal abstraction multivariate time series time intervals

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Deep anomaly detection in horizontal axis wind turbines using GraphConvolutional Autoencoders for multivariate time series

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能源与人工智能(英文) 2022年第2期8卷 79-91页

作者： Eric Stefan Miele Fabrizio Bonacina Alessandro Corsini Department of Mechanical and Aerospace Engineering Sapienza University of RomeVia Eudossiana 118RomeI00184Italy

Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring *** this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a Graph ConvolutionalAutoencoder for multivariate time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of Autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches.

关键词： Wind turbine Condition monitoring Deep anomaly detection SCADA data Graph Convolutional Autoencoder multivariate time series Early fault detection

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multivariate time series imputation for energy data using neural networks

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Energy and AI 2023年第3期13卷 25-35页

作者： Christopher Bulte Max Kleinebrahm Hasan Umitcan Yilmaz Juan Gomez-Romero Institute for Industrial Production(IIP) Chair of Energy EconomicsKarlsruhe Institute of Technology(KIT)Germany Department of Computer Science and Artificial Intelligence University of GranadaSpain

multivariate time series with missing values are common in a wide range of applications,including energy *** imputation methods often fail to focus on the temporal dynamics and the cross-dimensional correlation *** this paper we propose a two-step method based on an attention model to impute missing values in multivariate energy time ***,the underlying distribution of the missing values in the data is *** information is then further used to train an attention based imputation *** learning the distribution prior to the imputation process,the model can respond flexibly to the specific characteristics of the underlying *** developed model is applied to European energy data,obtained from the European Network of Transmission System Operators for *** different evaluation metrics and benchmarks,the conducted experiments show that the proposed model is preferable to the benchmarks and is able to accurately impute missing values.

关键词： Missing value estimation multivariate time series Neural networks Attention model Energy data

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Generating Adversarial Samples on multivariate time series using Variational Autoencoders

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IEEE/CAA Journal of Automatica Sinica 2021年第9期8卷 1523-1538页

作者： Samuel Harford Fazle Karim Houshang Darabi Department of Mechanical and Industrial Engineering University of Illinois at ChicagoChicagoIL 60607 USA

Classification models for multivariate time series have drawn the interest of many researchers to the field with the objective of developing accurate and efficient ***,limited research has been conducted on generating adversarial samples for multivariate time series classification *** samples could become a security concern in systems with complex sets of *** study proposes extending the existing gradient adversarial transformation network(GATN)in combination with adversarial autoencoders to attack multivariate time series classification *** proposed model attacks classification models by utilizing a distilled model to imitate the output of the multivariate time series classification *** addition,the adversarial generator function is replaced with a variational autoencoder to enhance the adversarial *** developed methodology is tested on two multivariate time series classification models:1-nearest neighbor dynamic time warping(1-NN DTW)and a fully convolutional network(FCN).This study utilizes 30 multivariate time series benchmarks provided by the University of East Anglia(UEA)and University of California Riverside(UCR).The use of adversarial autoencoders shows an increase in the fraction of successful adversaries generated on multivariate time *** the best of our knowledge,this is the first study to explore adversarial attacks on multivariate time ***,we recommend future research utilizing the generated latent space from the variational autoencoders.

关键词： Adversarial machine learning deep learning multivariate time series perturbation methods

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VAECGAN:a generating framework for long-term prediction in multivariate time series

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Cybersecurity 2021年第1期4卷 337-348页

作者： Xiang Yin Yanni Han Zhen Xu Jie Liu Institute of Information Engineering Chinese Academy of SciencesSchool of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina Institute of Information Engineering Chinese Academy of SciencesBeijingChina Network information department China Mobile Communications Group Co.LtdBeijingChina

Long-term prediction is still a difficult problem in data *** usually use various kinds of methods of Recurrent Neural Network to ***,with the increase of the prediction step,the accuracy of prediction decreases *** order to improve the accuracy of long-term prediction,we propose a framework Variational Auto-Encoder Conditional Generative Adversarial Network(VAECGAN).Our model is divided into three *** first part is the encoder net,which can encode the exogenous sequence into latent space vectors and fully save the information carried by the exogenous *** second part is the generator net which is responsible for generating prediction *** the third part,the discriminator net is used to classify and feedback,adjust data generation and improve prediction ***,extensive empirical studies tested with five real-world datasets(NASDAQ,SML,Energy,EEG,KDDCUP)demonstrate the effectiveness and robustness of our proposed approach.

关键词： Long-term prediction multivariate time series Attention mechanism Generating framework

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Rock burst Chaotic Prediction on multivariate time series and LSSVR 25th

Rock burst Chaotic Prediction on Multivariate Time Series an...

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第25届中国控制与决策会议

作者： Wang Wei Tao Hui Ma Xiao-ping School of Information and Electrical Engineering China University of Mining and Technology School of Electrical Engineering and Automation Henan Polytechnic University

ISBN: (纸本)9781467355339

State variables reconstructed by multivariate time series were used as LSSVR model inputs to predict the future value of rock burst monitor variables. First, the chaotic prediction principle on multivariate reconstruction and LSSVR was given. Then given the effects of reconstruction parameters on reconstruction results and LSSVR parameters on prediction error, genetic algorithm was adopted to determine reconstruction and LSSVR parameters simultaneously to ensure chaotic prediction accuracy. Finally, in Matlab2009b environment, based on the effectiveness verify of our method by Lorenz chaos system, Microseism time series were simulated to predict rock burst. The results show that the rock burst prediction method on multivariate time series reconstruction and LSSVR can accurately predict monitoring variables in advance to forecast rock burst even in the case of relatively short history data.

关键词： Rock burst chaotic prediction multivariate time series phase space reconstruction LSSVR genetic algorithm

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A improved common principal components based dimension reduction method for multivariate time series analysis

A improved common principal components based dimension reduc...

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第32届中国控制与决策会议

作者： Shengqiang Ye Ke Zhang School of Automation Chongqing University IEEE Key Laboratory of Complex System Safety and Control Ministry of Education School of Automation Chongqing University

Existing traditional dimension reduction methods for multivariate time series have limitations for principal feature preservation, and have impact on the quality of data mining. Therefore, from the perspective of shape features of data, a novel dimension reduction method of multivariate time series based on improved common principal components was proposed. In training datasets, centers for multi-time series of each category were obtained through the improved DTW Barycenter Averaging method. And then the common principal component analysis of the central time series in each category is carried out. In this way, the dimension of multi-time series can be reduced. The comparative experimental results show that the proposed method can reduce dimension effectively and achieve a good classification effect.

关键词： dimension reduction multivariate time series common principal components analysis classification

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