smooth: Forecasting Using State Space Models
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
The package includes ADAM (Svetunkov, 2021, <https://openforecast.org/adam/>),
Exponential Smoothing (Hyndman et al., 2008, <doi:10.1007/978-3-540-71918-2>),
SARIMA (Svetunkov & Boylan, 2019 <doi:10.1080/00207543.2019.1600764>),
Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi:10.13140/RG.2.2.24986.29123>),
Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi:10.1080/00207543.2017.1380326>)
and several simulation functions. It also allows dealing with intermittent demand based on the
iETS framework (Svetunkov & Boylan, 2019, <doi:10.13140/RG.2.2.35897.06242>).
Version: |
3.1.6 |
Depends: |
R (≥ 3.0.2), greybox (≥ 1.0.5) |
Imports: |
Rcpp (≥ 0.12.3), stats, generics (≥ 0.1.2), graphics, grDevices, pracma, statmod, MASS, nloptr, utils, zoo |
LinkingTo: |
Rcpp, RcppArmadillo (≥ 0.8.100.0.0) |
Suggests: |
legion, numDeriv, testthat, knitr, rmarkdown, doMC, doParallel, foreach |
Published: |
2022-03-30 |
Author: |
Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics
and Forecasting, Lancaster University, UK) |
Maintainer: |
Ivan Svetunkov <ivan at svetunkov.ru> |
BugReports: |
https://github.com/config-i1/smooth/issues |
License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: |
https://github.com/config-i1/smooth |
NeedsCompilation: |
yes |
Language: |
en-GB |
Materials: |
README NEWS |
In views: |
TimeSeries |
CRAN checks: |
smooth results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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