stR: Seasonal Trend Decomposition Using Regression
Methods for decomposing seasonal data: STR (a Seasonal-Trend
time series decomposition procedure based on Regression) and Robust STR. In
some ways, STR is similar to Ridge Regression and Robust STR can be related to
LASSO. They allow for multiple seasonal components, multiple linear covariates
with constant, flexible and seasonal influence. Seasonal patterns (for both
seasonal components and seasonal covariates) can be fractional and flexible
over time; moreover they can be either strictly periodic or have a more
complex topology. The methods provide confidence intervals for the estimated
components. The methods can also be used for forecasting.
| Version: |
0.7 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
compiler, foreach, forecast, graphics, grDevices, Matrix, methods, quantreg, SparseM, stats |
| Suggests: |
demography, doParallel, knitr, markdown, rgl, rmarkdown, seasonal, testthat |
| Published: |
2024-07-28 |
| DOI: |
10.32614/CRAN.package.stR |
| Author: |
Alexander Dokumentov
 [aut],
Rob Hyndman [aut,
cre] |
| Maintainer: |
Rob Hyndman <Rob.Hyndman at monash.edu> |
| BugReports: |
https://github.com/robjhyndman/stR/issues |
| License: |
GPL-3 |
| URL: |
https://pkg.robjhyndman.com/stR/,
https://github.com/robjhyndman/stR |
| NeedsCompilation: |
no |
| Citation: |
stR citation info |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
stR results |
Documentation:
Downloads:
Reverse dependencies:
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