[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119380-en":3,"doc-seo-119380-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119380,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Comparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study","An anomaly is an observation (or set of observations) that is unusual for a given dataset, and anomaly detection supports tasks in data preparation and risk identification such as credit card fraud detection and network intrusion detection. Multiple approaches exist, commonly grouped into statistical methods and machine learning algorithms, which are often used separately and compared infrequently. A simulation study was conducted to compare their performance. Data were generated with copula-based dependence structures while marginal distributions were manipulated to create different anomaly types.","Econometrics. Ekonometria. Advances in Applied Data Analysis  \nYear 2024, Vol. 28, No. 4 ISSN 2449-9994  \n[journals.ue.wroc.pl/eada](journals.ue.wroc.pl/eada)  \nComparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study  \nKlaudia Lenart  \nUniversity of Economics in Katowice, Doctoral School e-mail: [klaudia.lenart@edu.uekat.pl](klaudia.lenart@edu.uekat.pl)  \nORCID: 0000-0001-8135-9362  \n© 2024 Klaudia Lenart  \nThis work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit [http://creativecommons.org/licenses/by-sa/4.0/](http://creativecommons.org/licenses/by-sa/4.0/)  \nQuote as: Lenart, K. (2024) . Comparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study. Econometrics. Ekonometria. Advances in Applied Data Analysis, 28(4), 23-31.  \nDOI: 10. 15611/eada.2024.4.02  \nJEL: C15, C18  \nAbstract  \nAim: An anomaly is an observation or a group of observations that is unusual for a given dataset. Anomaly detection has many applications, not only as a step of data preparation but also, for example, as a way of identifying credit card fraud detection, network intrusions and much more. There are diverse methods of anomaly detection. In particular two groups of methods have been developed independently – statistical methods and machine learning algorithms. Those methods are rarely compared. While statistical methods focus on formulating a measure of the abnormality of the observations, supervised machine learning makes it possible to use data about typical observations and previously identified anomalies. The aim of this paper was to compare the two approaches by conducting a simulation study.  \nMethodology: A simulation study was conducted, during which the data was generated using copula functions. For the purpose of generating different types of anomalies, marginal distributions of the variables were manipulated. The effectiveness of each method was evaluated based on measures of classification model performance.  \nResults: While the accuracy of the statistical methods was dependent on the precise prediction of the percentage of the anomalies that would occur in the data, the machine learning algorithms’ recall was significantly lower when the change in the marginal distribution of the value parameters was smaller.  \nImplications and recommendations: For the statistical methods included in the study, knowledge about the distribution of the variables was crucial while the supervised machine learning algorithms required acquiring a training dataset. Unlike machine learning algorithms, the statistical methods performed with similar accuracy even when the change in the marginal distribution parameters’ value was smaller.  \nOriginality/value: The two approaches to anomaly detection presented in the paper are not often compared, usually used by two separate groups of researchers – statisticians and machine learning or data science specialists.  \nKeywords: anomaly detection, simulation study, machine learning  \n1. Introduction  \nMethods of anomaly detection have been developed in several different fields of study and therefore various approaches can be found in the literature. Most notably two fields of study: statistics and data science, gaining more and more popularity in recent years, have independently developed methods of anomaly detection. Although the most popular methods used by data scientists, for example machine learning algorithms, are unquestionably based on statistics and econometrics, researchers who focus on this subject matter are often not interested in statistical methods. Thus there are not many studies comparing the more traditional statistical methods with machine learning algorithms. The aim of this paper was to compare the accuracy of supervised learning machine learning algorithms with two chosen statistical methods of anomaly detection. Hence a simulati","cbCaijjshx7Uyvgr","https://ap.wps.com/l/cbCaijjshx7Uyvgr","pdf",607085,1,9,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"What problem does the paper address in anomaly detection?\",\"answer\":\"The paper compares two approaches to anomaly detection—statistical methods and supervised machine learning—because they are developed independently and rarely evaluated side by side.\"},{\"question\":\"How was the simulation study designed?\",\"answer\":\"The study generated data using copula functions for dependence, then produced different anomaly types by manipulating the marginal distributions of the variables.\"},{\"question\":\"What were the main findings when comparing the methods?\",\"answer\":\"Statistical methods’ accuracy depended on correctly predicting the anomaly percentage, while machine learning recall decreased when changes to the marginal distribution parameters were smaller.\"}]","Comparison of Machine Learning and Statistical 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problem does the paper address in anomaly detection?","Question",{"text":75,"@type":76},"The paper compares two approaches to anomaly detection—statistical methods and supervised machine learning—because they are developed independently and rarely evaluated side by side.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the simulation study designed?",{"text":80,"@type":76},"The study generated data using copula functions for dependence, then produced different anomaly types by manipulating the marginal distributions of the variables.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings when comparing the methods?",{"text":84,"@type":76},"Statistical methods’ accuracy depended on correctly predicting the anomaly percentage, while machine learning recall decreased when changes to the marginal distribution parameters were 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