An adaptive real-time outlier detection algorithm based on ARMA model for radar's health monitoring

Lv, Yongle

doi:10.1109/autest.2015.7356475

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…In [34], authors proposed an online adaptive algorithm based on the ARMA model, which can detect and replace outliers dynamically. In addition, this algorithm can achieve the demand of the real-time radar's healthcare application.…”

Section: Statistical-based Techniquesmentioning

confidence: 99%

A Survey of Outlier Detection Techniques in IoT: Review and Classification

Samara

Bennis

Abouaïssa

et al. 2022

JSAN

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The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these data are susceptible to different factors that may affect the collected data efficiency or the network functioning, and therefore the desired quality of service (QoS). In this context, one of the main issues requiring more research and adapted solutions is the outlier detection problem. The challenge is to detect outliers and classify them as either errors to be ignored, or important events requiring actions to prevent further service degradation. In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. First, we provide the fundamentals of outlier detection while discussing the different sources of an outlier, the existing approaches, how we can evaluate an outlier detection technique, and the challenges facing designing such techniques. Second, comparison and discussion of the most recent outlier detection techniques are presented and classified into seven main categories, which are: statistical-based, clustering-based, nearest neighbour-based, classification-based, artificial intelligent-based, spectral decomposition-based, and hybrid-based. For each category, available techniques are discussed, while highlighting the advantages and disadvantages of each of them. The related works for each of them are presented. Finally, a comparative study for these techniques is provided.

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Section: Statistical-based Techniquesmentioning

confidence: 99%

A Survey of Outlier Detection Techniques in IoT: Review and Classification

Samara

Bennis

Abouaïssa

et al. 2022

JSAN

View full text Add to dashboard Cite

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“…Statistical methods like auto regressive moving averages [ 27 , 28 , 29 , 30 ] and Linear regression models were proposed in [ 31 ] and used for outlier detection even if it is hard to detect polynomial functions in real time [ 32 , 33 ]. In general, most of the statistical methods are based on historical data and used for offline anomaly detection even if some of them were described as heavy online anomaly detection techniques [ 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

Jurj

Czumbil

Bargauan

et al. 2021

Sensors

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The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.

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Outlier Detection in Time-Series Data: Specific to Nearly Uniform Signals from the Sensors

Suman

Rajathilagam

2018

Computational Vision and Bio Inspired Computing

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An adaptive real-time outlier detection algorithm based on ARMA model for radar's health monitoring

Cited by 5 publications

References 6 publications

A Survey of Outlier Detection Techniques in IoT: Review and Classification

A Survey of Outlier Detection Techniques in IoT: Review and Classification

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

Outlier Detection in Time-Series Data: Specific to Nearly Uniform Signals from the Sensors

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