Journal article
Fast, effective, and coherent time series modelling using the sparsity-ranked lasso
Statistical modelling, Vol.25(2), pp.150-169
04/01/2025
DOI: 10.1177/1471082X231225307
Abstract
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.
Details
- Title: Subtitle
- Fast, effective, and coherent time series modelling using the sparsity-ranked lasso
- Creators
- Ryan Peterson - Colorado School of Public HealthJoseph Cavanaugh - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Statistical modelling, Vol.25(2), pp.150-169
- DOI
- 10.1177/1471082X231225307
- ISSN
- 1471-082X
- eISSN
- 1477-0342
- Publisher
- Sage
- Number of pages
- 20
- Language
- English
- Date published
- 04/01/2025
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center; Internal Medicine
- Record Identifier
- 9984914013602771
Metrics
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