论文标题
建模间隔趋势线：间隔时间序列的符号奇异频谱分析
Modeling Interval Trendlines: Symbolic Singular Spectrum Analysis for Interval Time Series
论文作者
论文摘要
在本文中，我们提出了间隔值时间序列的奇异频谱分析的扩展。所提出的方法可用于分解和预测控制设定的随机过程的动力学。间隔时间序列分解的结果组件可以理解为间隔趋势线，周期或噪声。预测可以通过线性复发方法进行，我们设计了用于多元设置的分解方法的概括。在模拟研究中展示了所提出的方法的性能。我们采用拟议的方法，以实时跟踪管理阿根廷股票市场（Merval）的动态，该案例研究涵盖了最近的湍流，导致阿根廷政府与国际货币基金进行了讨论。
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.
