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 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>), Vector Exponential Smoothing (de Silva et al., 2010, <doi:10.1177/1471082X0901000401>) in state space forms, several simulation functions and intermittent demand state space models. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2017, <doi:10.13140/RG.2.2.35897.06242>).

Version: 2.6.0
Depends: R (≥ 3.0.2), greybox (≥ 0.5.9)
Imports: Rcpp (≥ 0.12.3), stats, graphics, forecast (≥ 7.0), nloptr, utils, zoo
LinkingTo: Rcpp, RcppArmadillo (≥
Suggests: Mcomp, numDeriv, testthat, knitr, rmarkdown
Published: 2020-06-17
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK)
Maintainer: Ivan Svetunkov <ivan at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Language: en-GB
Materials: README NEWS
In views: TimeSeries
CRAN checks: smooth results


Reference manual: smooth.pdf
Vignettes: ces() - Complex Exponential Smoothing
es() - Exponential Smoothing
gum() - Generalised Univariate Model
Occurrence part of iETS model
Simulate functions of the package
sma() - Simple Moving Average
ssarima() - State-Space ARIMA
ves() - Vector Exponential Smoothing
Package source: smooth_2.6.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: smooth_2.6.0.tgz, r-oldrel: smooth_2.6.0.tgz
Old sources: smooth archive

Reverse dependencies:

Reverse depends: MAPA
Reverse suggests: greybox


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