Research on Time Series Anomaly Detection Algorithm and Application

Zhao, Zhiyang; Zhang, Yang; Zhu, Xianxun; Zuo, Jiancun

doi:10.1109/iaeac47372.2019.8997819

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…TSAD aims to identify unusual patterns or behaviors in sequential data [12][13][14][15][16][17]. TSAD has applications in various domains, such as smart grids, network security, finance, health care, and social media.…”

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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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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“…The proposed mechanism allows integration with c programming for flexibility and ease of use. However, it has a low detection capability of intrusions and attacks with incomplete and uncertain data or unknown environment information [18].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

et al. 2022

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Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms.

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“…However, in the case of input abnormal samples, since both the DE and E networks are only optimized for normal samples, the distance between x andx and z andẑ cannot be minimized. In summary, our objective function for the generator is as follows: L = w str L str + w con L con + w enc L enc (7) w str , w con and w enc are weighted parameters that adjust the influence of a single loss on the overall objective function.…”

Section: ) Struggle-lossmentioning

confidence: 99%

Self-Adaption AAE-GAN for Aluminum Electrolytic Cell Anomaly Detection

Cao

Liu

et al. 2021

IEEE Access

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Nowadays, the anomaly detection of aluminum electrolysis cell is a big problem in the aluminum electrolysis industry. The problem of unbalanced time series samples is common in industrial applications. The number of samples under normal conditions is much larger than that under abnormal conditions. In the electrolytic aluminum industry, this problem is even more serious, it is very difficult to find abnormal samples in industrial production because experts do not have a clear criterion to judge abnormalities. In traditional machine learning algorithms, such as support vector machine (SVM) and convolutional neural network (CNN), it is difficult to obtain high classification accuracy on the problem of class imbalance, and these methods tend to be more biased towards positive samples. In recent years, generative adversarial network (GAN) has become more and more popular in the field of anomaly detection. However, these methods need to find the best mapping from the actual space to the latent space in the anomaly detection stage, and the optimization process may bring new errors and take a long time. In this article, we use the ability of GAN to model complex high-dimensional image distribution, and propose a self-adaption AAE-GAN network based on adaptive changes of input samples. This time series anomaly detection method converts multi-dimensional time series data into a two-dimensional matrix, and only normal samples are needed in the training process, which effectively solves the above problems. The method we proposed is to use encoder and decoder to constitute a generator and a discriminator. During the training process, the generator and the discriminator are trained jointly and confrontationally, so that the mapping ability of the encoder can be fully reflected. In the anomaly detection stage, we determine whether the sample is abnormal according to the size of the reconstruction difference. Experimental results show that the detection accuracy and speed of this method are very high.INDEX TERMS Aluminum electrolytic cell, anomaly detection, AAE-GAN, multivariate time series, imbalanced industrial time series.

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Research on Time Series Anomaly Detection Algorithm and Application

Cited by 12 publications

References 14 publications

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

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

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

Self-Adaption AAE-GAN for Aluminum Electrolytic Cell Anomaly Detection

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