AI 中文总结
研究函数时间序列变化点检测问题，通过最大化给定其他参数时变化点的条件概率解决均值漂移和过程波动性问题，所提估计器具有一致性。
AI 中文摘要
本文研究了函数时间序列中的变化点检测问题，其中观测值在稀疏和密集支持上都允许变化。我们解决了均值漂移以及过程波动性问题。我们的方法基于在给定所有其他参数的情况下最大化变化点的条件概率。此外，已证明所提出的估计器是一致的。
英文摘要
In this paper we study the problem of change point detection in functional time series where the observations are allowed to vary on both sparse and dense support. We address the problem of mean shift as well as the process volatility. Our methodology is based on the maximization of the conditional probability of change point given all the other parameters. Further, it has been proved that the proposed estimator is consistent.
