论文标题
改进的在线罚款参数选择程序，用于$ \ ell_1 $ penalizatization AutoreSermister，带有外源变量
An Improved Online Penalty Parameter Selection Procedure for $\ell_1$-Penalized Autoregressive with Exogenous Variables
论文作者
论文摘要
高维统计时间序列文献中的许多最新发展围绕时间依赖性应用，这些应用程序可以适用于正规化的最小二乘正方形。特别值得关注的是套索，这既可以正规化并提供特征选择。套索需要规范惩罚参数，该参数确定施加的稀疏度。尊重时间依赖性的最流行的惩罚参数选择方法在计算上非常密集，不适合对某些类别的时间序列进行建模。我们提出了增强典型的时间序列模型，即具有外源变量的自回归模型，具有新颖的在线惩罚参数选择程序，它利用了时间序列数据的顺序性质，以提高计算性能和相对于仿真应用中现有方法的计算性能和预测精度，并具有宏观经济指标。
Many recent developments in the high-dimensional statistical time series literature have centered around time-dependent applications that can be adapted to regularized least squares. Of particular interest is the lasso, which both serves to regularize and provide feature selection. The lasso requires the specification of a penalty parameter that determines the degree of sparsity to impose. The most popular penalty parameter selection approaches that respect time dependence are very computationally intensive and are not appropriate for modeling certain classes of time series. We propose enhancing a canonical time series model, the autoregressive model with exogenous variables, with a novel online penalty parameter selection procedure that takes advantage of the sequential nature of time series data to improve both computational performance and forecast accuracy relative to existing methods in both a simulation and empirical application involving macroeconomic indicators.
