In config-i1/smooth: Forecasting Using State Space Models

knitr::opts_chunk$set(fig.width=6, fig.height=4, fig.path='Figs/', fig.show='hold',
                      warning=FALSE, message=FALSE)

This vignette covers ces() and auto.ces() functions, which are part of smooth package.

Let's load the necessary packages:

require(smooth)

ces() function allows constructing Complex Exponential Smoothing either with no seasonality, or with simple/partial/full one. A simple call for ces() results in estimation of non-seasonal model:

For the same series from M3 dataset ces() can be constructed using:

ces(BJsales, h=12, holdout=TRUE, silent=FALSE)

This output is very similar to ones printed out by adam() function. The only difference is complex smoothing parameter values which are printed out instead of persistence vector in adam().

If we want automatic model selection, then we use auto.ces() function:

auto.ces(AirPassengers, h=12, holdout=TRUE, silent=FALSE)

By default, the function optimises the initial values, but other options ("backcasting" and "complete") are supported as well:

ces(BJsales, h=12, holdout=TRUE, initial="back")

The function also works with explanatory variables if the data frame or a matrix is provided instead of the vector of values:

BJData <- cbind(y=BJsales, x=BJsales.lead)
cesModel <- ces(BJData, h=12, holdout=TRUE, regressors="use")

Finally, all the main methods for the adam function are supported by ces() as well. For example, here how we can produce prediction interval:

forecast(cesModel, h=12, interval="pred") |> plot()

config-i1/smooth documentation built on June 11, 2025, 9:52 a.m.