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 over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.

中文翻译：

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建模区间趋势线：区间时间序列的符号奇异谱分析

在本文中，我们提出了对区间值时间序列的奇异谱分析的扩展。所提出的方法可用于分解和预测控制集值随机过程的动力学。区间时间序列分解的结果成分可以理解为区间趋势线、周期或噪声。预测可以通过线性循环方法进行，我们设计了多变量设置分解方法的推广。在模拟研究中展示了所提出方法的性能。我们应用所提出的方法来实时跟踪阿根廷股票市场 (MERVAL) 的动态，在一段动荡时期的案例研究中，导致阿根廷政府与国际货币基金组织进行了讨论。