In this paper we introduce a general model framework based on Self Organizing Maps (SOMs) to explore the behavior of populations mortality rates and life expectancy. In particular, we show how to employ SOM clustering capabilities to construct coherent mortality rates, i.e. mortality rates that can be applied unchanged to a wide range of countries. To such purpose, we will employ various countries mortality data downloaded from the Human Mortality Database. Our aim is two– fold. On the one hand, we are going to prove that a data mining approach can be meaningful to build mortality forecasts in a way which is less pretending (in terms of both computing time and parameters to estimate) than traditional techniques. This issue is very important, provided that mortality forecasts are widely employed to develop insurance products. On the other hand, we will show that SOM clustering can be very effective to extract similar mortality patterns from apparently very different countries, thus highlighting non–linear hidden features that are missing for more standard techniques

A model for mortality forecasting based on Self Organizing Maps

RESTA, MARINA;RAVERA, MARINA
2013

Abstract

In this paper we introduce a general model framework based on Self Organizing Maps (SOMs) to explore the behavior of populations mortality rates and life expectancy. In particular, we show how to employ SOM clustering capabilities to construct coherent mortality rates, i.e. mortality rates that can be applied unchanged to a wide range of countries. To such purpose, we will employ various countries mortality data downloaded from the Human Mortality Database. Our aim is two– fold. On the one hand, we are going to prove that a data mining approach can be meaningful to build mortality forecasts in a way which is less pretending (in terms of both computing time and parameters to estimate) than traditional techniques. This issue is very important, provided that mortality forecasts are widely employed to develop insurance products. On the other hand, we will show that SOM clustering can be very effective to extract similar mortality patterns from apparently very different countries, thus highlighting non–linear hidden features that are missing for more standard techniques
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/471119
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