An underlying assumption in Multivariate Singular Spectrum Analysis (MSSA) is that the time series are governed by a linear recurrent continuation. However, in the presence of a structural break, multiple series can be transferred from one homogeneous state to another over a comparatively short time breaking this assumption. As a consequence, forecasting performance can degrade significantly. In this paper, we propose a state-dependent model to incorporate the movement of states in the linear recurrent formula called a State-Dependent Multivariate SSA (SD-MSSA) model. The proposed model is examined for its reliability in the presence of a structural break by conducting an empirical analysis covering both synthetic and real data. Comparison with standard MSSA, BVAR, VAR and VECM models shows the proposed model outperforms all three models significantly.

中文翻译：

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状态相关的线性递归公式，适用于具有结构中断的时间序列

多元奇异谱分析 (MSSA) 中的一个基本假设是时间序列由线性循环延续控制。然而，在存在结构断裂的情况下，多个系列可以在相对较短的时间内从一种同质状态转移到另一种状态，从而打破这一假设。因此，预测性能可能会显着下降。在本文中，我们提出了一种状态相关模型，将状态的运动纳入称为状态相关多元 SSA (SD-MSSA)的线性循环公式中模型。通过对合成数据和真实数据进行实证分析，对所提出的模型在存在结构断裂的情况下的可靠性进行了检查。与标准 MSSA、BVAR、VAR 和 VECM 模型的比较表明，所提出的模型显着优于所有三种模型。