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Keywords = magnitude outlyingness

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27 pages, 23296 KB  
Article
Functional Data Analysis for the Detection of Outliers and Study of the Effects of the COVID-19 Pandemic on Air Quality: A Case Study in Gijón, Spain
by Xurxo Rigueira, María Araújo, Javier Martínez, Paulino José García-Nieto and Iago Ocarranza
Mathematics 2022, 10(14), 2374; https://doi.org/10.3390/math10142374 - 6 Jul 2022
Cited by 9 | Viewed by 3695
Abstract
Air pollution, especially at the ground level, poses a high risk for human health as it can have serious negative effects on the population of certain areas. The high variability of this type of data, which are affected by weather conditions and human [...] Read more.
Air pollution, especially at the ground level, poses a high risk for human health as it can have serious negative effects on the population of certain areas. The high variability of this type of data, which are affected by weather conditions and human activities, makes it difficult for conventional methods to precisely detect anomalous values or outliers. In this paper, classical analysis, statistical process control, and functional data analysis are compared for this purpose. The results obtained motivate the development of a new outlier detector based on the concept of functional directional outlyingness. The validation of this algorithm is perfomed on real air quality data from the city of Gijón, Spain, aiming to detect the proven reduction in NO2 levels during the COVID-19 lockdown in that city. Three more variables (SO2, PM10, and O3) are studied with this technique. The results demonstrate that functional data analysis outperforms the two other methods, and the proposed outlier detector is well suited for the accurate detection of outliers in data with high variability. Full article
(This article belongs to the Section E2: Control Theory and Mechanics)
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19 pages, 808 KB  
Article
Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping
by Álvaro Rollón de Pinedo, Mathieu Couplet, Bertrand Iooss, Nathalie Marie, Amandine Marrel, Elsa Merle and Roman Sueur
Appl. Sci. 2021, 11(23), 11475; https://doi.org/10.3390/app112311475 - 3 Dec 2021
Cited by 5 | Viewed by 2679
Abstract
Finding outliers in functional infinite-dimensional vector spaces is widely present in the industry for data that may originate from physical measurements or numerical simulations. An automatic and unsupervised process of outlier identification can help ensure the quality of a dataset (trimming), validate the [...] Read more.
Finding outliers in functional infinite-dimensional vector spaces is widely present in the industry for data that may originate from physical measurements or numerical simulations. An automatic and unsupervised process of outlier identification can help ensure the quality of a dataset (trimming), validate the results of industrial simulation codes, or detect specific phenomena or anomalies. This paper focuses on data originating from expensive simulation codes to take into account the realistic case where only a limited quantity of information about the studied process is available. A detection methodology based on different features, such as h-mode depth or the dynamic time warping, is proposed to evaluate the outlyingness both in the magnitude and shape senses. Theoretical examples are used to identify pertinent feature combinations and showcase the quality of the detection method with respect to state-of-the-art methodologies of detection. Finally, we show the practical interest of the method in an industrial context thanks to a nuclear thermal-hydraulic use case and how it can serve as a tool to perform sensitivity analysis on functional data. Full article
(This article belongs to the Special Issue Unsupervised Anomaly Detection)
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