This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that text data can be a useful addition to more traditional numerical data.

翻译：本文介绍了可能在不同频率取样的高维时间序列数据的结构化机算学习回归。 分散的LASSO测算器可以利用这种时间序列数据结构,并优于无结构的LASSO。 我们在一个允许混合过程的框架范围内,为分散的LASSO测算器建立甲骨文不平等,并承认金融和宏观经济数据可能比指数尾巴要重。 在现在预测美国GDP增长的实验应用中,测算器与其他替代数据相比表现优异,文本数据可以成为更传统的数字数据的有益补充。