A0467
Title: Clustering and forecasting multiple functional time series
Authors: Chen Tang - The Australian National University (Australia) [presenting]
Han Lin Shang - Australian National University (Australia)
Yanrong Yang - The Australian National University (Australia)
Abstract: Modelling and forecasting homogeneous age-specific mortality rates of multiple countries could lead to improvements in long-term forecasting. Data fed into joint models are often grouped according to nominal attributes, which may still contain heterogeneity and deteriorate the forecast results. To address this issue, a novel clustering technique is proposed to pursue homogeneity among multiple functional time series based on functional panel data modelling. Common functional time series features can be extracted using a functional panel data model with fixed effects. These common features could be decomposed into two components: the functional time trend and the mode of variations of functions. The functional time trend reflects the dynamics across time, while the functional pattern captures the fluctuations within curves. The proposed clustering method searches for homogeneous age-specific mortality rates of multiple countries by accounting for both features. The proposed clustering technique outperforms other existing methods through a Monte Carlo simulation and could handle complicated cases with slow decaying eigenvalues. In empirical data analysis, it is found that the clustering results of age-specific mortality rates can be explained by the combination of geographic region, ethnic groups, and socioeconomic status. Further, it is shown that the model produces more accurate forecasts than several benchmark methods in forecasting age-specific mortality rates.
