Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection

Kieu, Tung; Yang, Bin; Guo, Chenjuan; Jensen, Christian S.; Zhao, Yan; Huang, Feiteng; Zheng, Kai

doi:10.1109/icde53745.2022.00273

Cited by 33 publications

(7 citation statements)

References 35 publications

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“…Consequently, this technique can produce new datasets containing segments of similar lengths. Regarding the detection of contextual outliers, neural networks such as autoencoder-based [ 40 ] or LSTM-based models [ 41 ] can be utilized to assess and compare the effects of automatic anomaly detection on the prediction models.…”

Section: Discussionmentioning

confidence: 99%

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

et al. 2022

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Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals’ lengths for specific concentrations. To find the optimal network, we altered the following variables: the LSTM layer structure (unidirectional LSTM (LSTM) and bidirectional LSTM (BLSTM)), optimizers (Adam, RMSPROP, SGDM), number of hidden units, and amount of augmented data. Then, the evaluation of the networks revealed that the highest original data accuracy increased from 50% to 92% by exploiting the data augmentation method. In addition, the SGDM optimizer showed a lower performance prediction than that of the ADAM and RMSPROP algorithms, and the number of hidden units was ineffective in improving the networks’ performances. Moreover, the BLSTM nets showed more accurate predictions than those of the ULSTM nets on lengthier signals. These results demonstrate that this method can automatically detect the analyte concentration from the sensor signals.

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Section: Discussionmentioning

confidence: 99%

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

et al. 2022

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“…The proposed approach was evaluated on two real-world time series repositories, the univariate time series repository Numenta Anomaly Benchmark (NAB) [84] and the multivariate time series repository ECG [85], and achieved an average AUC-ROC and AUC-PR of 0.97 and 0.777 respectively. Kieu et al [86] presented an EoAE approach to detect anomalies in ECG signals. They employ multiple variants of CAE for feature extraction and anomaly detection.…”

Section: E Ecgmentioning

confidence: 99%

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Nawaz,

Khan,

Ahmad

2024

IEEE Access

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In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an aberration from normal behavior, or an unexpected observation requiring immediate attention. The detection of anomalies in biomedical data has a direct impact on the health and safety of individuals. However, anomalous events are rare, diverse, and infrequent. Often, the collection of anomalous data may involve significant loss of human life and healthcare costs. Therefore, traditional supervised machine and deep learning algorithms may not be directly applicable to such problems. Biomedical data are often collected in the form of images, electronic health records, and time series. Typically, an autoencoder (AE) or its corresponding variant is trained on normal data, and an anomaly is identified as a significant deviation from these data based on reconstruction error or other metrics. An Ensemble of AEs (EoAEs) can serve as a robust approach for anomaly detection in biomedical data by combining diverse and accurate views of normal data. An EoAE can provide superior detection to a single encoder; however, its performance can depend on various factors, including the diversity of the created data, the accuracy of the individual AEs, and the combination of their outcomes. Herein, we perform a comprehensive narrative literature review on the use of EoAEs when using different types of biomedical data. Such an ensemble provides a promising approach for anomaly detection in biomedical data, offering the potential for performance improvement by leveraging the strengths of diverse AEs. However, several challenges remain, such as the need for data specification and determination of the optimal number of AEs in the ensemble. By addressing these challenges, researchers can enhance the effectiveness of EoAEs for anomaly detection in various types of biomedical data. Furthermore, through this review, we highlight the significance of evaluating and comparing the performance of an EoAE with that of single AEs by establishing agreed-upon evaluation metrics and investigating normalization techniques for anomaly scores. We conclude the review by presenting challenges and open questions in the field with for future research.

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“…AE can also be extended to variational AE (VAE) [11], which imposes a probabilistic distribution on the latent space and can generate realistic data samples. Robust AE (RAE) [20], a method inspired by Robust Principal Component Analysis (RPCA) [21], used an AE and an error matrix to separate the error sequence from the original sequence. The LSTM-AE-ADVanced (LSTM-AE-ADV) method proposed by Kieu T et al suggested that using static methods to enrich datasets and feed them into AE could achieve better results [22].…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection of Liquid Level in Mold during Continuous Casting by Using Forecasting and Error Generation

Kang

Yuan

et al. 2023

Applied Sciences

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Anomaly detection of liquid levels in molds is an important task in continuous casting. Data that consists of a series of liquid levels in mold during a continuous casting process can be viewed as a time series, on which Time Series Anomaly Detection (TSAD) methods can be applied. However, the abnormal and normal data in the liquid data in the mold sequence share similar features. And due to manual control limitations, the anomaly sequence in liquid level in mold data lasts longer. Therefore, using existing TSAD methods based on AutoEncoders (AEs) often results in high false positive rates. In this paper, a novel framework is proposed for anomaly detection of liquid level in mold by using unsupervised deep-learning-based TSAD. The framework decomposes a time series into normal and error sequences. A forecasting network reconstructs the normal sequence to solve the first issue, which allows the proposed method to consider the context. An error extraction network generates errors from the original sequence to solve the second issue. It removes anomalies from the original sequence during training to prevent anomaly pollution and allows the forecasting network’s training to be free from anomaly pollution. A new dynamic threshold method is proposed to identify anomalies. The proposed method is evaluated on the actual casting dataset by comparing it with baseline methods. The experiment results indicate that the proposed framework outperforms some of the best anomaly detection methods in terms of accuracy, precision, and F1 score.

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Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection

Cited by 33 publications

References 35 publications

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Anomaly Detection of Liquid Level in Mold during Continuous Casting by Using Forecasting and Error Generation

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