Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model

Lin, Shu‐Yu; Clark, Ronald; Birke, Robert; Schönborn, Sandro; Trigoni, Niki; Roberts, Stephen

doi:10.1109/icassp40776.2020.9053558

Cited by 163 publications

(69 citation statements)

References 10 publications

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“…This section covers the extensive experiments conducted to test and compare the performance of DQR-AD with six other state-of-the-art anomaly detection methods that model prediction error using Gaussian distribution to identify anomalies. These methods include DeepAnT [33], NumentaTM [52], ContextOSE [58], EXPoSE [59], AE [38], and VAE-LSTM [39]. The experiment is conducted using real and synthetic datasets from different application domain.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, for each timeseries point, an anomaly score is calculated which helps in handling the type of anomaly detection problem. Deep learning-based anomaly detection techniques using long short-term memory (LSTM) [28][29][30] and other forms of recurrent neural network (RNN) [31,32], convolutional neural network (CNN) [33], and autoencoder [15,[34][35][36][37][38][39] demonstrate higher performance over the previously mentioned prediction-base anomaly detection techniques. Despite their performance and unsupervised learning approach, they compute the anomaly score by assuming a distribution usually of Gaussian type on the prediction error, which is either ranked, or threshold to label data instances as anomalous or not.…”

Section: Introductionmentioning

confidence: 99%

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Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series

Tambuwal

Neagu

2021

SN COMPUT. SCI.

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Time-series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep neural networks (DNNs: e.g., RNN, CNN, and Autoencoder). The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminative features and time-series temporal nature. However, their performance is affected by usually assuming a Gaussian distribution on the prediction error, which is either ranked, or threshold to label data instances as anomalous or not. An exact parametric distribution is often not directly relevant in many applications though. This will potentially produce faulty decisions from false anomaly predictions due to high variations in data interpretation. The expectations are to produce outputs characterized by a level of confidence. Thus, implementations need the Prediction Interval (PI) that quantify the level of uncertainty associated with the DNN point forecasts, which helps in making better-informed decision and mitigates against false anomaly alerts. An effort has been made in reducing false anomaly alerts through the use of quantile regression for identification of anomalies, but it is limited to the use of quantile interval to identify uncertainties in the data. In this paper, an improve time-series anomaly detection method called deep quantile regression anomaly detection (DQR-AD) is proposed. The proposed method go further to used quantile interval (QI) as anomaly score and compare it with threshold to identify anomalous points in time-series data. The tests run of the proposed method on publicly available anomaly benchmark datasets demonstrate its effective performance over other methods that assumed Gaussian distribution on the prediction or reconstruction cost for detection of anomalies. This shows that our method is potentially less sensitive to data distribution than existing approaches.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series

Tambuwal

Neagu

2021

SN COMPUT. SCI.

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“…It is worth noting that OOD detection is often conflated with novelty, anomaly, or outlier detection in the literature [ 34 ]. However, predictions in the case of novelty or outlier detection in time series data often implies identifying deviation from an expected input based on a chosen error metric [ 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors

Boyer

Burns

Whyne

2021

Sensors

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Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data from activity classes that are not represented in the training data of a Machine Learning (ML) algorithm. OOD data are a challenge to classify accurately for most ML algorithms, especially deep learning models that are prone to overconfident predictions based on in-distribution (IIN) classes. To simulate the OOD problem in physiotherapy, our team collected a new dataset (SPARS9x) consisting of inertial data captured by smartwatches worn by 20 healthy subjects as they performed supervised physiotherapy exercises (IIN), followed by a minimum 3 h of data captured for each subject as they engaged in unrelated and unstructured activities (OOD). In this paper, we experiment with three traditional algorithms for OOD-detection using engineered statistical features, deep learning-generated features, and several popular deep learning approaches on SPARS9x and two other publicly-available human activity datasets (MHEALTH and SPARS). We demonstrate that, while deep learning algorithms perform better than simple traditional algorithms such as KNN with engineered features for in-distribution classification, traditional algorithms outperform deep learning approaches for OOD detection for these HAR time series datasets.

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“…This allows unsupervised diagnostics to be performed in cases where there are no labeled data for training. Lin et al [ 27 ] used a variational autoencoders (VAE) module for forming robust local features over short windows and an LSTM module for estimating the long-term correlations in the series on top of the features inferred from the VAE module. P.T.…”

Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation

Ahn

Yeo²

2021

Sensors

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As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.

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Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model

Cited by 163 publications

References 10 publications

Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series

Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series

Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors

Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation

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