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

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<rdf:Description>
<dc:creator>Owadally, Iqbal</dc:creator>
<dc:creator>Zhou, Feng</dc:creator>
<dc:creator>Otunba, Rasaq</dc:creator>
<dc:creator>Lin, Jessica</dc:creator>
<dc:creator>Wright, Douglas</dc:creator>
<dc:date>2019-09-02</dc:date>
<dc:description xml:lang="es">Sumario: 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. </dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/170008.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:subject xml:lang="es">Modelos actuariales</dc:subject>
<dc:subject xml:lang="es">Big data</dc:subject>
<dc:subject xml:lang="es">Matemática del seguro</dc:subject>
<dc:subject xml:lang="es">Empresas de seguros</dc:subject>
<dc:subject xml:lang="es">Análisis de riesgos</dc:subject>
<dc:subject xml:lang="es">Estados Unidos</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Time series data mining with an application to the measurement of underwriting cycles</dc:title>
<dc:relation xml:lang="es">En: North American actuarial journal. - Schaumburg : Society of Actuaries, 1997- = ISSN 1092-0277. - 02/09/2019 Tomo 23 Número 3 - 2019 , p. 469-484</dc:relation>
<dc:coverage xml:lang="es">Estados Unidos</dc:coverage>
</rdf:Description>
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