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 ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), 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>) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).

Package details

AuthorIvan Svetunkov [aut, cre] (Senior Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK)
MaintainerIvan Svetunkov <ivan@svetunkov.com>
LicenseLGPL-2.1
Version4.1.0
URL https://github.com/config-i1/smooth
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("smooth")

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smooth documentation built on Oct. 1, 2024, 5:07 p.m.