Time series data mining with an application to the measurement of underwriting cycles
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| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20200000068 | ||
| 003 | MAP | ||
| 005 | 20220911211326.0 | ||
| 008 | 200102e20190902usa|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 245 | 0 | 0 | $aTime series data mining with an application to the measurement of underwriting cycles$cIqbal Owadally... [et al.] |
| 520 | $aUnderwriting 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. | ||
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20080592011$aModelos actuariales | |
| 650 | 4 | $0MAPA20140022717$aBig data | |
| 650 | 4 | $0MAPA20080602437$aMatemática del seguro | |
| 650 | 4 | $0MAPA20080590567$aEmpresas de seguros | |
| 650 | 4 | $0MAPA20080588953$aAnálisis de riesgos | |
| 651 | 1 | $0MAPA20080638337$aEstados Unidos | |
| 700 | 1 | $0MAPA20130010328$aOwadally, Iqbal | |
| 700 | 1 | $0MAPA20200000167$aZhou, Feng | |
| 700 | 1 | $0MAPA20200000174$aOtunba, Rasaq | |
| 700 | 1 | $0MAPA20200000181$aLin, Jessica | |
| 700 | 1 | $0MAPA20200000198$aWright, Douglas | |
| 773 | 0 | $wMAP20077000239$tNorth American actuarial journal$dSchaumburg : Society of Actuaries, 1997-$x1092-0277$g02/09/2019 Tomo 23 Número 3 - 2019 , p. 469-484 |