Forecasting Using State Space Models
Multiple seasonal classical decomposition
Simulate Exponential Smoothing
Simulate Generalised Exponential Smoothing
Simulate Occurrence Part of ETS model
Simulate Simple Moving Average
Simulate SSARIMA
Simple Moving Average
Smooth package
Combination of forecasts of state space models
Function returns the ultimate answer to any question
State Space ARIMA
Error measures for an estimated model
ADAM is Augmented Dynamic Adaptive Model
Complex Exponential Smoothing Auto
Automatic GUM
State Space ARIMA
Complex Exponential Smoothing
Centered Moving Average
Exponential Smoothing in SSOE state space model
Forecasting time series using smooth functions
Generalised Univariate Model
Smooth classes checkers
Multiple Seasonal ARIMA
Function returns the multiple steps ahead covariance matrix of forecas...
Occurrence ETS model
Occurrence ETS, general model
Functions that extract values from the fitted model
Plots for the fit and states
Prediction Likelihood Score
Reapply the model with randomly generated initial parameters and produ...
Objects exported from other packages
Multiple steps ahead forecast errors
Simulate Complex Exponential Smoothing
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>).