smooth: Forecasting Using Smoothing Functions

Functions implementing Single Source of Error state-space models for purposes of time series analysis and forecasting. The package includes exponential smoothing, SARIMA in state-space forms and several simulation functions.

Version: 2.4.1
Depends: R (≥ 3.0.2)
Imports: Rcpp (≥ 0.12.3), stats, graphics, forecast, nloptr, utils, zoo
LinkingTo: Rcpp, RcppArmadillo (≥
Suggests: Mcomp, numDeriv, testthat, knitr, rmarkdown
Published: 2018-03-06
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_2.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: smooth_2.4.1.tgz, r-oldrel: smooth_2.4.1.tgz
Old sources: smooth archive

Reverse dependencies:

Reverse depends: MAPA
Reverse suggests: greybox


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