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Sliced average variance estimation for multivariate time series

机译：多变量时间序列的切片平均方差估计

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摘要

Supervised dimension reduction for time series is challenging as there may be temporal dependence between the response y and the predictors . Recently a time series version of sliced inverse regression, TSIR, was suggested, which applies approximate joint diagonalization of several supervised lagged covariance matrices to consider the temporal nature of the data. In this paper, we develop this concept further and propose a time series version of sliced average variance estimation, TSAVE. As both TSIR and TSAVE have their own advantages and disadvantages, we consider furthermore a hybrid version of TSIR and TSAVE. Based on examples and simulations we demonstrate and evaluate the differences between the three methods and show also that they are superior to apply their iid counterparts to when also using lagged values of the explaining variables as predictors.
机译：时间序列的监督尺寸减少是具有挑战性的，因为响应y和预测因子之间可能存在时间依赖性。最近，建议了一个时间序列版本的切片逆回归，这提出了几个监督的滞后协方差矩阵的近似关节对角线，以考虑数据的时间性。在本文中，我们进一步开发了这一概念，并提出了一个时间序列版本的切片平均方差估计，Tsave。随着TSIR和TSAVE都有自己的优点和缺点，我们考虑到TSIR和TSAVE的混合版本。基于实施例和仿真，我们展示并评估了三种方法之间的差异，并且还优于应用其IID对应物，当使用解释变量的滞后值作为预测因子时。

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