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bat365在线平台、所2024年系列学术活动(第046场):崔嫣 博士后 加拿大阿尔伯塔大学

发表于: 2024-05-13   点击: 

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bat365中文官方网站,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。