srlTS: Sparsity-Ranked Lasso for Time Series

An implementation of sparsity-ranked lasso for time series data. This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7> in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2023+) <doi:10.48550/arXiv.2211.01492>, which also describes this package in greater detail. The Sparsity-Ranked Lasso (SRL) for Time Series implemented in 'srlTS' can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The SRL is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature.

Version: 0.1.1
Depends: R (≥ 3.5)
Imports: dplyr, methods, ncvreg, RcppRoll, rlang, yardstick
Suggests: covr, kableExtra, knitr, magrittr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-12-14
Author: Ryan Andrew Peterson ORCID iD [aut, cre, cph]
Maintainer: Ryan Andrew Peterson <ryan.a.peterson at>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README NEWS
CRAN checks: srlTS results


Reference manual: srlTS.pdf
Vignettes: Simple Case Studies
Time Series Modeling with Multiple Modes


Package source: srlTS_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): srlTS_0.1.1.tgz, r-oldrel (arm64): srlTS_0.1.1.tgz, r-release (x86_64): srlTS_0.1.1.tgz


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