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: Linking:Please use the canonical form https://CRAN.R-project.org/package=stR to link to this page.
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