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 |
| DOI: | 10.32614/CRAN.package.srlTS |
| Author: | Ryan Andrew Peterson
 [aut, cre,
cph] |
| Maintainer: | Ryan Andrew Peterson <ryan.a.peterson at cuanschutz.edu> |
| BugReports: | https://github.com/petersonR/srlTS/issues |
| License: | GPL (≥ 3) |
| URL: | https://petersonr.github.io/srlTS/, https://github.com/petersonR/srlTS/ |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | srlTS results [issues need fixing before 2024-12-11] |
Documentation:
| Reference manual: | srlTS.pdf |
| Vignettes: |
Simple Case Studies Time Series Modeling with Multiple Modes |
Downloads:
| Package source: | srlTS_0.1.1.tar.gz |
| Windows binaries: | r-devel: srlTS_0.1.1.zip, r-release: srlTS_0.1.1.zip, r-oldrel: srlTS_0.1.1.zip |
| 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, r-oldrel (x86_64): srlTS_0.1.1.tgz |
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