smooth (2.5.4)

Forecasting Using State Space Models.

https://github.com/config-i1/smooth
http://cran.r-project.org/web/packages/smooth

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, ), SARIMA (Svetunkov & Boylan, 2019 ), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, ), Simple Moving Average (Svetunkov & Petropoulos, 2018 ), Vector Exponential Smoothing (de Silva et al., 2010, ) 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, ).

Maintainer: Ivan Svetunkov
Author(s): Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK)

License: GPL (>= 2)

Uses: forecast, greybox, nloptr, Rcpp, zoo, numDeriv, Mcomp, testthat, knitr, rmarkdown
Reverse depends: MAPA
Reverse suggests: greybox

Released about 1 month ago.