A0816
Title: Optimal forecasting for locally stationary functional time series using double-sieve method
Authors: Yan Cui - Harbin Institute of Technology (China) [presenting]
Zhou Zhou - University of Toronto (Canada)
Abstract: Accurate curve forecasting is of vital importance for policy planning, decision making and resource allocation in many engineering and industrial applications. In this paper we establish a theoretical foundation for the optimal short-term linear prediction of non-stationary functional or curve time series with smoothly timevarying data generating mechanisms. The core of this work is to establish a unified functional auto-regressive approximation result for a general class of non-stationary functional time series. A double-sieve expansion method is proposed and theoretically verified for the asymptotic optimal forecasting. A telecommunication traffic data set is used to illustrate the usefulness of the proposed theory and methodology.
