| Name : R-srlTS
| |
| Version : 0.1.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.4
| Date : 2024-06-14 12:02:25
|
| Group : Development/Libraries/Other
| Source RPM : R-srlTS-0.1.1-lp153.2.4.src.rpm
|
| Size : 0.59 MB
| |
| Packager : (none)
| |
| Summary : Sparsity-Ranked Lasso for Time Series
|
Description :
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
