报告题目：Optimal Short-term Forecast for Locally Stationary Functional Time Series
报 告 人：崔嫣 博士后 加拿大阿尔伯塔大学
报告时间：2024年5月16日 10:00-11:00
报告地点：数学楼第二报告厅
校内联系人：朱复康 fzhu@jlu.edu.cn
报告摘要：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.
报告人简介：崔嫣，阿尔伯塔大学数学与统计科学系博士后。2020年博士毕业于吉林大学，2021-2022曾在哈尔滨工业大学任教，随后曾在多伦多大学从事博士后工作。主要研究领域为时间序列分析，目前主要从事函数型时间序列的研究。
