報告題目：Optimal Short-term Forecast for Locally Stationary Functional Time Series
報 告 人：崔嫣 博士後 加拿大阿爾伯塔大學
報告時間：2024年5月16日 10:00-11:00
報告地點：數學樓第二報告廳
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：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 time-varying data generating mechanisms. The core of this work is to establish a unified functional auto-regressive approximation result for a general class of locally 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.
報告人簡介：崔嫣，阿爾伯塔大學數學與統計科學系博士後。2020年博士畢業于吉林大學，2021-2022曾在哈爾濱工業大學任教，随後曾在多倫多大學從事博士後工作。主要研究領域為時間序列分析，目前主要從事函數型時間序列的研究。
