区间趋势线建模：区间时间序列的符号奇异谱分析
Modeling Interval Trendlines: Symbolic Singular Spectrum Analysis for Interval Time Series
摘要：
在这篇文章中，我们提出了一个扩展的奇异谱分析的区间值时间序列。所提出的方法可以用来分解和预测集值随机过程的动力学。区间时间序列被分解的结果分量可以被理解为区间趋势线、周期或噪声。预测可以通过线性递归方法进行，我们设计了多变量设置的分解方法的概括。所提出的方法的性能展示在模拟研究。我们应用所提出的方法，以跟踪动态管理阿根廷股市（MERVAL）在实时，在一个案例研究，涵盖了最近一段时间的动荡，导致阿根廷政府与国际货币基金组织的讨论。
英文摘要：
In this article we propose an extension of singular spectrum analysis for interval-valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set-valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study that covers the most recent period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
