The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet of the SRL is that an algorithm should be more skeptical of higher-order polynomials and interactions *a priori* compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In time series, the same idea of ranked prior skepticism can be applied to the possibly seasonal autoregressive (AR) structure of the series during the model fitting process, becoming especially useful in settings with uncertain or multiple modes of seasonality. The SRL can naturally incorporate exogenous variables, with streamlined options for inference and/or feature selection. The fitting process is quick even for large series with a high-dimensional feature set. In this work, we discuss both the formulation of this procedure and the software we have developed for its implementation via the **fastTS** R package. We explore the performance of our SRL-based approach in a novel application involving the autoregressive modeling of hourly emergency room arrivals at the University of Iowa Hospitals and Clinics. We find that the SRL is considerably faster than its competitors, while producing more accurate predictions.

翻译：稀疏度排序LASSO（SRL）最初被开发用于存在交互项和多项式项的模型选择与估计。其核心原则是：算法应*先验地*对高阶多项式与交互项持有比主效应更强的怀疑态度，因此纳入这些更复杂项需要更高水平的证据。在时间序列中，这一排序先验怀疑思想可应用于序列在模型拟合过程中可能存在的季节性自回归（AR）结构——在季节性模式不确定或存在多种季节性模式的场景中尤为实用。SRL能自然纳入外生变量，并提供简化的推断和/或特征选择选项。即使对于具有高维特征集的大规模序列，其拟合过程也十分快速。本文既阐述该方法的公式化框架，也介绍我们为其实施开发的**fastTS** R语言软件包。我们通过一项创新应用——对爱荷华大学医院与诊所每小时急诊室到达人数进行自回归建模——来探索基于SRL方法的表现。研究发现，SRL在产生更准确预测的同时，其运算速度显著优于同类方法。