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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>).
Getting started
README.md
Augmented Dynamic Adaptive Model
ces() - Complex Exponential Smoothing
es() - Exponential Smoothing
gum() - Generalised Univariate Model
oes() - occurrence part of iETS model
Simulate functions of the package
sma() - Simple Moving Average
smooth: forecasting using state-space models
ssarima() - State-Space ARIMA
Browse package contents
Package details |
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| Author | Ivan Svetunkov [aut, cre] (Senior Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK) |
| Maintainer | Ivan Svetunkov <ivan@svetunkov.com> |
| License | LGPL-2.1 |
| Version | 4.1.0 |
| URL | https://github.com/config-i1/smooth |
| Package repository | View on CRAN |
| Installation |
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