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 sceptical 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 scepticism can be applied to characterize the potentially complex seasonal autoregressive (AR) structure of a 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 modelling 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 generally producing more accurate predictions.

中文翻译：

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使用稀疏排序套索进行快速、有效且连贯的时间序列建模

稀疏排序套索 (SRL) 是为了在存在交互作用和多项式的情况下进行模型选择和估计而开发的。SRL 的主要原则是，与主效应相比，算法应该对高阶多项式和先验相互作用更加怀疑，因此包含这些更复杂的术语应该需要更高级别的证据。在时间序列中，排序先验怀疑论的相同思想可用于在模型拟合过程中表征序列的潜在复杂季节性自回归 (AR) 结构，这在具有不确定或多种季节性模式的环境中变得特别有用。SRL 可以自然地合并外生变量，并提供简化的推理和/或特征选择选项。即使对于具有高维特征集的大系列，拟合过程也很快。在这项工作中，我们讨论了该过程的制定以及我们为通过 fastTS R 包实现该过程而开发的软件。我们探讨了基于 SRL 的方法在一项新颖应用中的性能，该应用涉及爱荷华大学医院和诊所每小时到达急诊室的自回归模型。我们发现 SRL 比其竞争对手要快得多，同时通常会产生更准确的预测。