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<article article-type="research-article" dtd-version="1.2" xml:lang="ru" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><front><journal-meta><journal-id journal-id-type="issn">2518-1092</journal-id><journal-title-group><journal-title>Research result. Information technologies</journal-title></journal-title-group><issn pub-type="epub">2518-1092</issn></journal-meta><article-meta><article-id pub-id-type="doi">10.18413/2518-1092-2022-8-3-0-2</article-id><article-id pub-id-type="publisher-id">3220</article-id><article-categories><subj-group subj-group-type="heading"><subject>INFORMATION SYSTEM AND TECHNOLOGIES</subject></subj-group></article-categories><title-group><article-title>&lt;strong&gt;ON THE INFORMATIVE FREQUENCY PORTRAIT APPLICATION FOR THE EMPIRICAL DATA SEGMENTS LOCALIZATION&lt;/strong&gt;</article-title><trans-title-group xml:lang="en"><trans-title>&lt;strong&gt;ON THE INFORMATIVE FREQUENCY PORTRAIT APPLICATION FOR THE EMPIRICAL DATA SEGMENTS LOCALIZATION&lt;/strong&gt;</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Ursol</surname><given-names>Denis Vladimirovich</given-names></name><name xml:lang="en"><surname>Ursol</surname><given-names>Denis Vladimirovich</given-names></name></name-alternatives><email>ursoldenis@mail.ru</email></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Tikhonsky</surname><given-names>Nikolay Antonovich</given-names></name><name xml:lang="en"><surname>Tikhonsky</surname><given-names>Nikolay Antonovich</given-names></name></name-alternatives><email>n.tikhonskiy@mail.ru</email></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Chernomorets</surname><given-names>Daria Andreevna</given-names></name><name xml:lang="en"><surname>Chernomorets</surname><given-names>Daria Andreevna</given-names></name></name-alternatives><email>daria013ch@yandex.ru</email></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Bolgova</surname><given-names>Evgeniya Vitalievna</given-names></name><name xml:lang="en"><surname>Bolgova</surname><given-names>Evgeniya Vitalievna</given-names></name></name-alternatives><email>Bolgova_e@bsuedu.ru</email></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Chernomorets</surname><given-names>Andrey Alekseevich</given-names></name><name xml:lang="en"><surname>Chernomorets</surname><given-names>Andrey Alekseevich</given-names></name></name-alternatives><email>Chernomorets@bsu.edu.ru</email></contrib></contrib-group><pub-date pub-type="epub"><year>2023</year></pub-date><volume>8</volume><issue>3</issue><fpage>0</fpage><lpage>0</lpage><self-uri content-type="pdf" xlink:href="/media/information/2023/3/ИТ_НР_8.3_2_quEyWNN.pdf" /><abstract xml:lang="ru"><p>The problem of localization (location determination) in a registered numerical series of a given series of smaller dimensions often arises when processing empirical data of various nature. In this paper, an algorithm for the empirical data segments localization, which are represented by numerical series, is developed. The developed algorithm is based on the calculation of the similarity measure of the analyzed numerical series features. As a set of features characterizing a numerical series, it is proposed to use an informative frequency portrait of a segment of numerical data, for the calculation of which the corresponding relations based on a discrete cosine transformation are given in the work. The paper provides examples of calculating an informative frequency portrait for various numerical series (precedents). The results of computational experiments presented in this paper demonstrate the operability of the developed algorithm for estimating the localization of segments of numerical series based on the use of an informative frequency portrait with discrete cosine transformation.</p></abstract><trans-abstract xml:lang="en"><p>The problem of localization (location determination) in a registered numerical series of a given series of smaller dimensions often arises when processing empirical data of various nature. In this paper, an algorithm for the empirical data segments localization, which are represented by numerical series, is developed. The developed algorithm is based on the calculation of the similarity measure of the analyzed numerical series features. As a set of features characterizing a numerical series, it is proposed to use an informative frequency portrait of a segment of numerical data, for the calculation of which the corresponding relations based on a discrete cosine transformation are given in the work. The paper provides examples of calculating an informative frequency portrait for various numerical series (precedents). The results of computational experiments presented in this paper demonstrate the operability of the developed algorithm for estimating the localization of segments of numerical series based on the use of an informative frequency portrait with discrete cosine transformation.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>numerical series</kwd><kwd>precedent</kwd><kwd>localization</kwd><kwd>discrete cosine transform</kwd><kwd>informative frequency portrait</kwd><kwd>localization error</kwd></kwd-group><kwd-group xml:lang="en"><kwd>numerical series</kwd><kwd>precedent</kwd><kwd>localization</kwd><kwd>discrete cosine transform</kwd><kwd>informative frequency portrait</kwd><kwd>localization error</kwd></kwd-group></article-meta></front><back><ref-list><title>Список литературы</title><ref id="B1"><mixed-citation>1. Abramov G.V., Korobova L.A., Ivashin A.L., Matytsina I.A. 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