Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

Ding, Nan; Gao, Huanbo; Bu, Hongyu; Ma, Haoxuan; Si, Huaiwei

doi:10.3390/s18103367

Cited by 42 publications

(20 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We took out the correlation between abnormal and normal before the abnormality from GAT, respectively, and drew the heat map in Figure 5. e right side of Figure 5 shows the correlation between feature 1 and features 2, 3,4,5,6,16,17,18,19,20, and 21 at normal time, while the correlation was at abnormal time on the left. e darker the color block, the higher the correlation between features, and vice versa.…”

Section: Gatmentioning

confidence: 98%

“…Hundman et al [5] used the long-and short-time memory network (LSTM) to detect the spacecraft multivariate time series based on prediction loss. Ding et al [6] proposed RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN), which improved the performance of real-time anomaly detection. However, most of the proposed methods often rely on the RNN (Recurrent Neural Network) learning properties and distribution in temporal pattern; relationship between sequences is still unutilized.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern

2020

View full text Add to dashboard Cite

Currently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads to false warnings. Our article proposes an unsupervised multivariate time series anomaly detection. In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root mean square error between the predicted value and the actual value to perform extreme value analysis to obtain the threshold. Finally, the model in this paper outperforms other latest models on actual datasets.

show abstract

Section: Gatmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern

2020

View full text Add to dashboard Cite

show abstract

“…The conditional probabilities are retrieved efficiently from a tree structure. Scores, computed using Equation (12) for each PST P i 1 ≤ i ≤ n, concerning every TS from a common subject are stored in an array. The mean of the array is the multivariate score for the subject, being obtained by 1…”

Section: Comparison With Probabilistic Suffix Treesmentioning

confidence: 99%

“…Data instances are scored given their distance to the boundary, typical in clustering and classification methods [9,10]. Recent efforts have been invested trying to satisfy the existing gap in MTS anomaly detection [11][12][13]; however, a complete and available implementation of such approaches is non-existing. This forces analysts to use typical univariate strategies.…”

Section: Introductionmentioning

confidence: 99%

Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks

2021

View full text Add to dashboard Cite

Outliers are observations suspected of not having been generated by the underlying process of the remaining data. Many applications require a way of identifying interesting or unusual patterns in multivariate time series (MTS), now ubiquitous in many applications; however, most outlier detection methods focus solely on univariate series. We propose a complete and automatic outlier detection system covering the pre-processing of MTS data that adopts a dynamic Bayesian network (DBN) modeling algorithm. The latter encodes optimal inter and intra-time slice connectivity of transition networks capable of capturing conditional dependencies in MTS datasets. A sliding window mechanism is employed to score each MTS transition gradually, given the DBN model. Two score-analysis strategies are studied to assure an automatic classification of anomalous data. The proposed approach is first validated in simulated data, demonstrating the performance of the system. Further experiments are made on real data, by uncovering anomalies in distinct scenarios such as electrocardiogram series, mortality rate data, and written pen digits. The developed system proved beneficial in capturing unusual data resulting from temporal contexts, being suitable for any MTS scenario. A widely accessible web application employing the complete system is publicly available jointly with a tutorial.

show abstract

“…-Ability to retrieve data speedily. Queries should be answered as fast as possible, as the TSDB might be the cornerstone of further systems or operations, such as data exploration or visualization, data analysis, or machine learning techniques such as predictive maintenance or anomaly detection [19,20].…”

mentioning

confidence: 99%

NagareDB: A Resource-Efficient Document-Oriented Time-Series Database

Calatrava

Fontal

Cucchietti

et al. 2021

Data

View full text Add to dashboard Cite

The recent great technological advance has led to a broad proliferation of Monitoring Infrastructures, which typically keep track of specific assets along time, ranging from factory machinery, device location, or even people. Gathering this data has become crucial for a wide number of applications, like exploration dashboards or Machine Learning techniques, such as Anomaly Detection. Time-Series Databases, designed to handle these data, grew in popularity, becoming the fastest-growing database type from 2019. In consequence, keeping track and mastering those rapidly evolving technologies became increasingly difficult. This paper introduces the holistic design approach followed for building NagareDB, a Time-Series database built on top of MongoDB—the most popular NoSQL Database, typically discouraged in the Time-Series scenario. The goal of NagareDB is to ease the access to three of the essential resources needed to building time-dependent systems: Hardware, since it is able to work in commodity machines; Software, as it is built on top of an open-source solution; and Expert Personnel, as its foundation database is considered the most popular NoSQL DB, lowering its learning curve. Concretely, NagareDB is able to outperform MongoDB recommended implementation up to 4.7 times, when retrieving data, while also offering a stream-ingestion up to 35% faster than InfluxDB, the most popular Time-Series database. Moreover, by relaxing some requirements, NagareDB is able to reduce the disk space usage up to 40%.

show abstract

Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

Cited by 42 publications

References 16 publications

MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern

MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern

Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks

NagareDB: A Resource-Efficient Document-Oriented Time-Series Database

Contact Info

Product

Resources

About