smooth (2.5.4)

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, ), 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.