A0958
Title: Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality
Authors: Han Lin Shang - Macquarie University (Australia) [presenting]
Ying Sun - KAUST (Saudi Arabia)
Cristian Felipe Jimenez Varon - King Abdullah University of Science and Technology (Saudi Arabia)
Abstract: The focus is on modelling and forecasting high-dimensional functional time series (HDFTS), which can be cross-sectionally correlated and temporally dependent. A novel two-way functional median polish decomposition, which is robust against outliers, is presented to decompose HDFTS into deterministic and time-varying components. A functional time series forecasting method, based on dynamic functional principal component analysis, is implemented to produce forecasts for the time-varying components. By combining the forecasts of the time-varying components with the deterministic components, forecast curves for multiple populations are jointly obtained. Illustrated by the sex- and age-specific mortality rates in the US, France, and Japan, which contain 51 states, 95 departments, and 47 prefectures, respectively, the proposed model delivers more accurate point and interval forecasts in forecasting multi-population mortality than several benchmark methods.
