论文标题
部分可观测时空混沌系统的无模型预测
Fast same-step forecast in SUTSE model and its theoretical properties
论文作者
论文摘要
我们考虑通过看似无关的时间序列方程（SUTSE）模型预测多元时间序列的问题。 SUTSE模型通常假设误差变量相关。一个至关重要的问题是，由于矩阵计算大量计算，模型估计需要大量的计算负载，尤其是对于高维数据。为了减轻计算问题，我们提出了一个两阶段的预测程序。首先，我们执行卡尔曼过滤器，好像误差变量是不相关的。也就是说，单变量时间序列分析是单独进行的，以避免进行大型矩阵计算。接下来，通过使用预测误差的分布来计算预测值。所提出的算法比普通的SUTSE模型快得多，因为我们不需要大型矩阵计算。提出了我们提出的估计量的一些理论特性。进行蒙特卡洛模拟以研究我们提出的方法的有效性。我们提出的程序的有用性通过总线拥塞数据申请说明。
We consider the problem of forecasting multivariate time series by a Seemingly Unrelated Time Series Equations (SUTSE) model. The SUTSE model usually assumes that error variables are correlated. A crucial issue is that the model estimation requires heavy computational loads because of a large matrix computation, especially for high-dimensional data. To alleviate the computational issue, we propose a two-stage procedure for forecasting. First, we perform the Kalman filter as if error variables are uncorrelated; that is, univariate time-series analyses are conducted separately to avoid a large matrix computation. Next, the forecast value is computed by using a distribution of forecast error. The proposed algorithm is much faster than the ordinary SUTSE model because we do not require a large matrix computation. Some theoretical properties of our proposed estimator are presented. Monte Carlo simulation is performed to investigate the effectiveness of our proposed method. The usefulness of our proposed procedure is illustrated through a bus congestion data application.
