A seminar by Cristian Felipe Jimenez Varon from King Abdullah University of Science and Technology
Title: Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality
Abstract: We explore the modeling and forecasting of high-dimensional functional time series (HDFTS), which can be cross-sectionally correlated and temporally dependent. We present a novel two-way functional median polish decomposition, which is robust against outliers, 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, we obtain forecast curves for multiple populations. We apply the model to age- and sex-specific mortality rates in the US, France, and Japan, in which there are 51 states, 95 departments, and 47 prefectures, respectively, to illustrate that the proposed model delivers more accurate point and interval forecasts in forecasting multi-population mortality than several benchmark methods.
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