AN EVALUATION OF UNSUPERVISED OUTLIER DETECTION METHODS FOR UNIVARIATE TIME SERIES DATA IN FINANCIAL TRANSACTIONS

dc.contributor.author Amankossova A.
dc.contributor.authorTuran C.
dc.date.accessioned2024-01-05T03:46:36Z
dc.date.available2024-01-05T03:46:36Z
dc.date.issued2023
dc.description.abstractAbstract. An essential problem in finance application areas is identifying abnormal subsequences in time series data. Despite the wide range of outlier detection algorithms, no substantial research has been conducted to thoroughly investigate and assess the various methodologies, particularly in the financial industry. This study focuses on comparing and contrasting the outcomes of various unsupervised algorithms. The findings reveal that the Local Outlier Factor technique outperforms the other methods in terms of precision, recall, and Fl-score. The research provides valuable insights for financial institutions and businesses looking to improve their identification of abnormalities systems and highlights the importance of choosing the appropriate unsupervised outlier detection method for financial transaction data. The outcomes of this study can be used to inform future research and development in the area of financial unusual case detection.
dc.identifier.citationA. Amankossova , C. Turan / AN EVALUATION OF UNSUPERVISED OUTLIER DETECTION METHODS FOR UNIVARIATE TIME SERIES DATA IN FINANCIAL TRANSACTIONS / СДУ хабаршысы - 2023
dc.identifier.issn2709-2631
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/1102
dc.language.isoen
dc.publisherСДУ хабаршысы - 2023
dc.subjectunivariate time series
dc.subjectcomparison
dc.subjectdetection techniques
dc.subjectanomaly
dc.subjectfinancial industry
dc.subjectСДУ хабаршысы - 2023
dc.subject№1
dc.titleAN EVALUATION OF UNSUPERVISED OUTLIER DETECTION METHODS FOR UNIVARIATE TIME SERIES DATA IN FINANCIAL TRANSACTIONS
dc.typeArticle

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