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, SARIMA, Complex Exponential Smoothing, Simple Moving Average, Vector Exponential Smoothing in state space forms, several simulation functions and intermittent demand state space models.

Version: 2.4.4
Depends: R (≥ 3.0.2), greybox (≥ 0.2.2)
Imports: Rcpp (≥ 0.12.3), stats, graphics, forecast, nloptr, utils, zoo
LinkingTo: Rcpp, RcppArmadillo (≥
Suggests: Mcomp, numDeriv, testthat, knitr, rmarkdown
Published: 2018-06-03
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
Materials: README NEWS
In views: TimeSeries
CRAN checks: smooth results


Reference manual: smooth.pdf
Vignettes: ces() - Complex Exponential Smoothing
es() - Exponential Smoothing
ges() - Generalised Exponential Smoothing
simulate(),, sim.ssarima(), sim.ces() - simulate functions for ETS, SARIMA and CES
sma() - Simple Moving Average
ssarima() - State-Space ARIMA
ves() - Vector Exponential Smoothing
Package source: smooth_2.4.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: smooth_2.4.4.tgz, r-oldrel: smooth_2.4.4.tgz
Old sources: smooth archive

Reverse dependencies:

Reverse depends: MAPA
Reverse suggests: greybox


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