| Name : R-fastTS
| |
| Version : 1.0.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.2
| Date : 2024-06-14 11:59:20
|
| Group : Development/Libraries/Other
| Source RPM : R-fastTS-1.0.1-lp153.2.2.src.rpm
|
| Size : 0.63 MB
| |
| Packager : (none)
| |
| Summary : Fast Time Series Modeling for Seasonal Series with Exogenous Variables
|
Description :
An implementation of sparsity-ranked lasso and related methods 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 (2024)
< doi:10.1177/1471082X231225307>, which also describes this package in
greater detail. The sparsity-ranked penalization methods for time
series implemented in \'fastTS\' can fit large/complex/high-frequency
time series quickly, even with a high-dimensional exogenous feature
set. The method 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 |
