An Exact Multivariate Model-Based Structural Decomposition
一种精确的多元模型结构分解
We propose a simple and structured procedure for decomposing a vector of time series into trend, cycle, seasonal, and irregular components. Contrary to common practice, we do not assume these components to be orthogonal conditional on their past. However, the state–space representation employed ensures that their estimates converge to values with null variances and covariances. Null variances are very important, as they ensure that the components do not change when the…