LSTM-Based VAE-GAN for Time-Series Anomaly Detection

Niu, Zijian; Yu, Ke; Wu, Xiaofei

doi:10.3390/s20133738

Cited by 103 publications

(53 citation statements)

References 24 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…VAE-GAN [9] aims to resolve the problem of errors in the mapping of GAN from realtime space to potential space. PAD [33] considers two aspects: state prediction and anomaly detection.…”

Section: Methods Flowmentioning

confidence: 99%

“…Although the method based on supervised learning has achieved good performance in anomaly detection, it needs a lot of labeled data for training. LSTM-based VAE-GAN [9] regards the long short-term memory (LSTM) network as the encoder, generator, and discriminator of VAE-GAN, and jointly trains the encoder, generator, and discriminator. In the anomaly detection stage, anomalies are detected based on reconstruction errors and discrimination results.…”

Section: Related Workmentioning

confidence: 99%

“…For example, KPI-TSAD [32] solves the problem of data imbalance through VAE. LSTM-based VAE-GAN [9] aims to resolve the problem of errors in the mapping of GAN from real-time space to potential space. PAD [33] considers two aspects: state prediction and anomaly detection.…”

Section: Related Workmentioning

confidence: 99%

“…In order to solve this problem, researchers combine LSTM [7] and VAE. Specifically, LSTM is used to replace the feedforward neural network in VAE, which can extract the characteristics, such as time dependence and correlation between data [8,9]. However, when VAE combines with the strong autoregressive decoder (LSTM), KL (Kullback-Leibler) divergence will disappear [10].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD

et al. 2021

View full text Add to dashboard Cite

Key performance indicator (KPI) anomaly detection is the underlying core technology in Artificial Intelligence for IT operations (AIOps). It has an important impact on subsequent anomaly location and root cause analysis. Variational auto-encoder (VAE) is a symmetry network structure composed of encoder and decoder, which has attracted extensive attention because of its ability to capture complex KPI data features and better detection results. However, VAE is not well applied to the modeling of KPI time series data and it is often necessary to set the threshold to obtain more accurate results. In response to these problems, this paper proposes a novel hybrid method for KPI anomaly detection based on VAE and support vector data description (SVDD). This method consists of two modules: a VAE reconstructor and SVDD anomaly detector. In the VAE reconstruction module, firstly, bi-directional long short-term memory (BiLSTM) is used to replace the traditional feedforward neural network in VAE to capture the time correlation of sequences; then, batch normalization is used at the output of the encoder to prevent the disappearance of KL (Kullback–Leibler) divergence, which prevents ignoring latent variables to reconstruct data directly. Finally, exponentially weighted moving average (EWMA) is used to smooth the reconstruction error, which reduces false positives and false negatives during the detection process. In the SVDD anomaly detection module, smoothed reconstruction errors are introduced into the SVDD for training to determine the threshold of adaptively anomaly detection. Experimental results on the public dataset show that this method has a better detection effect than baseline methods.

show abstract

“…VAE-GAN [9] aims to resolve the problem of errors in the mapping of GAN from realtime space to potential space. PAD [33] considers two aspects: state prediction and anomaly detection.…”

Section: Methods Flowmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Some anomaly detection methods based on supervised learning [3,4] can perform fast and accurate anomaly detection by relying on a large number of types of anomaly-labeled data, but they are not suitable for the actual operation and maintenance environment, which contains fewer anomalies. The new unsupervised and semi-supervised learning anomaly detection methods [5][6][7][8] can better adapt to the actual operation and maintenance environment. Some models use RNN and LSTM to analyze time series data, but RNN and LSTM have problems, such as error accumulation and the need for a lot of training memory, which leads to some false positives and false negatives in anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM

Shang

Zhang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

KPIs (Key Performance Indicators) in distributed systems may involve a variety of anomalies, which will lead to system failure and huge losses. Detecting KPI anomalies in the system is very important. This paper presents a time series anomaly detection method based on correlation analysis and HMM. Correlation analysis is used to obtain the correlation between abnormal KPIs in the system, thereby reducing the false alarm rate of anomaly detection. The HMM (Hidden Markov Model) is used for anomaly detection by finding the close relationship between abnormal KPIs. In our correlation analysis of abnormal KPIs, firstly, the time series prediction model (1D-CNN-TCN) is proposed. The residual sequence is obtained by calculating the residual between the predicted value and the actual value. The residual sequence can highlight the abnormal segment in each data point and improve the accuracy of anomaly screening. According to the obtained residual sequence, these abnormal KPIs are preliminarily screened out from the historical data. Next, KPI correlation analysis is performed, and the correlation score is obtained by adding a sliding window onto the obtained anomaly index residual sequence. The correlation analysis based on the residual sequence can eliminate the interference of the original data fluctuation itself. Then, a correlation matrix of abnormal KPIs is constructed using the obtained correlation scores. In anomaly detection, the constructed correlation matrix is processed to obtain the adaptive parameters of the HMM model, and the trained HMM is used to quickly discover the abnormal KPI that may cause a KPI anomaly. Experiments on public data sets show that the method obtains good results.

show abstract

Deep Anomaly Detection on Tennessee Eastman Process Data

Hartung

Franks

Tobias

et al. 2023

Chemie Ingenieur Technik

View full text Add to dashboard Cite

This paper provides the first comprehensive evaluation and analysis of modern (deep‐learning‐based) unsupervised anomaly detection methods for chemical process data. We focus on the Tennessee Eastman process dataset, a standard litmus test to benchmark anomaly detection methods for nearly three decades. Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications. From the benchmark, we conclude that reconstruction‐based methods are the methods of choice, followed by generative and forecasting‐based methods.

show abstract

LSTM-Based VAE-GAN for Time-Series Anomaly Detection

Cited by 103 publications

References 24 publications

A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD

A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD

Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM

Deep Anomaly Detection on Tennessee Eastman Process Data

Contact Info

Product

Resources

About