Time series data mining with an application to the measurement of underwriting cycles

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<title>Time series data mining with an application to the measurement of underwriting cycles</title>
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<namePart>Owadally, Iqbal</namePart>
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<namePart>Zhou, Feng</namePart>
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<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20200000174">
<namePart>Otunba, Rasaq</namePart>
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<namePart>Lin, Jessica</namePart>
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<namePart>Wright, Douglas</namePart>
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<abstract displayLabel="Summary">Underwriting cycles are believed to pose a risk management challenge to property-casualty insurers. The classical statistical methods that are used to model these cycles and to estimate their length assume linearity and give inconclusive results. Instead, this article proposes to use novel time series data Mining algorithms to detect and estimate periodicity on U.S. property-casualty insurance markets. These algorithms are in increasing use in data science and are applied to Big Data. </abstract>
<note type="statement of responsibility">Iqbal Owadally... [et al.]</note>
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<topic>Empresas de seguros</topic>
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<topic>Análisis de riesgos</topic>
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<title>North American actuarial journal</title>
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<identifier type="issn">1092-0277</identifier>
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<text>02/09/2019 Tomo 23 Número 3 - 2019 , p. 469-484</text>
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