标题： 未来曲线部分观测下的功能时间序列预测 显示英文标题

标题： Functional time series prediction under partial observation of the future curve

摘要： 提供可靠的预测是函数时间序列分析中的一个基本主题。 现有的函数时间序列方法旨在根据一组观察到的函数来预测一个完整的未来函数观测值。 这里讨论的兴趣问题是，如何将预测方法推广到部分信息可用于下一个轨迹的情况，以提高预测准确性。 所提出的方法结合了“下一区间”预测和完全函数回归预测，从而使部分观察到的部分有助于生成对未来曲线未观察部分的更好猜测。 基于最小化预测误差的自动选择准则有助于选择未知的调参。 模拟结果显示，与现有方法相比，所提出的方法在未观察部分的均方预测误差方面表现更优，其实际应用价值在环境和交通流量数据的分析中得到了说明。 显示英文摘要

摘要： Providing reliable predictions is one of the fundamental topics in functional time series analysis. Existing functional time series methodology seeks to predict a complete future functional observation based on a set of observed functions. The problem of interest discussed here is how to advance prediction methodology to cases where partial information on the next trajectory is available, with the aim of improving prediction accuracy. The proposed method combines "next-interval" prediction and fully functional regression prediction, so that the partially observed part can aid in producing a better guess for the unobserved part of the future curve. An automatic selection criterion based on minimizing the prediction error helps select unknown tuning parameters. Simulations indicate that the proposed method can outperform existing methods with respect to mean-square prediction error of the unobserved part, and its practical usefulness is illustrated in an analysis of environmental and traffic flow data.