利用稀疏性排序套索进行快速、有效和连贯的时间序列建模

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY
Ryan Peterson, Joseph Cavanaugh
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引用次数: 0

摘要

稀疏性排序套索(SRL)是针对存在交互作用和多项式时的模型选择和估计而开发的。SRL 的主要原理是,与主效应相比,算法应该先验地对高阶多项式和交互作用持怀疑态度,因此纳入这些更复杂的项需要更高水平的证据。在时间序列中,同样的先验怀疑排序思想也可用于在模型拟合过程中描述序列潜在的复杂季节性自回归(AR)结构,在季节性不确定或具有多种模式的情况下尤其有用。SRL 可以自然地纳入外生变量,并简化推理和/或特征选择的选项。即使是具有高维特征集的大型序列,其拟合过程也非常快速。在这项工作中,我们将讨论这一程序的表述,以及我们通过 fastTS R 软件包为实现这一程序而开发的软件。我们在爱荷华大学医院和诊所对每小时急诊到达人数进行自回归建模的新应用中,探索了基于 SRL 方法的性能。我们发现 SRL 比其竞争对手快得多,同时通常能产生更准确的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast, effective, and coherent time series modelling using the sparsity-ranked lasso
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.
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来源期刊
Statistical Modelling
Statistical Modelling 数学-统计学与概率论
CiteScore
2.20
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: The primary aim of the journal is to publish original and high-quality articles that recognize statistical modelling as the general framework for the application of statistical ideas. Submissions must reflect important developments, extensions, and applications in statistical modelling. The journal also encourages submissions that describe scientifically interesting, complex or novel statistical modelling aspects from a wide diversity of disciplines, and submissions that embrace the diversity of applied statistical modelling.
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