  Forecasting accuracy of mortality data is important for the management of
pension funds and pricing of life insurance in actuarial science. Age-specific
mortality forecasting in the US poses a challenging problem in high dimensional
time series analysis. Prior attempts utilize traditional dimension reduction
techniques to avoid the curse of dimensionality, and then mortality forecasting
is achieved through features' forecasting. However, a method of reducing
dimension pertinent to ideal forecasting is elusive. To address this, we
propose a novel approach to pursue features that are not only capable of
representing original data well but also capturing time-serial dependence as
most as possible. The proposed method is adaptive for the US mortality data and
enjoys good statistical performance. As a comparison, our method performs
better than existing approaches, especially in regard to the Lee-Carter Model
as a benchmark in mortality analysis. Based on forecasting results, we generate
more accurate estimates of future life expectancies and prices of life
annuities, which can have great financial impact on life insurers and social
securities compared with using Lee-Carter Model. Furthermore, various
simulations illustrate scenarios under which our method has advantages, as well
as interpretation of the good performance on mortality data.
