Description Usage Arguments Details Value Author(s) References See Also Examples
Function estimates CES in state space form with information potential equal to errors with different seasonality types and chooses the one with the lowest IC value.
1 2 3 4 5 6 7 8  auto.ces(y, models = c("none", "simple", "full"),
initial = c("backcasting", "optimal"), ic = c("AICc", "AIC", "BIC",
"BICc"), loss = c("likelihood", "MSE", "MAE", "HAM", "MSEh", "TMSE",
"GTMSE", "MSCE"), h = 10, holdout = FALSE, cumulative = FALSE,
interval = c("none", "parametric", "likelihood", "semiparametric",
"nonparametric"), level = 0.95, bounds = c("admissible", "none"),
silent = c("all", "graph", "legend", "output", "none"), xreg = NULL,
xregDo = c("use", "select"), initialX = NULL, ...)

y 
Vector or ts object, containing data needed to be forecasted. 
models 
The vector containing several types of seasonality that should be used in CES selection. See ces for more details about the possible types of seasonal models. 
initial 
Can be either character or a vector of initial states. If it
is character, then it can be 
ic 
The information criterion used in the model selection procedure. 
loss 
The type of Loss Function used in optimization. There are also available analytical approximations for multistep functions:
Finally, just for fun the absolute and half analogues of multistep estimators
are available: 
h 
Length of forecasting horizon. 
holdout 
If 
cumulative 
If 
interval 
Type of interval to construct. This can be:
The parameter also accepts 
level 
Confidence level. Defines width of prediction interval. 
bounds 
What type of bounds to use in the model estimation. The first letter can be used instead of the whole word. 
silent 
If 
xreg 
The vector (either numeric or time series) or the matrix (or
data.frame) of exogenous variables that should be included in the model. If
matrix included than columns should contain variables and rows  observations.
Note that 
xregDo 
The variable defines what to do with the provided xreg:

initialX 
The vector of initial parameters for exogenous variables.
Ignored if 
... 
Other nondocumented parameters. For example 
The function estimates several Complex Exponential Smoothing in the state space 2 described in Svetunkov, Kourentzes (2015) with the information potential equal to the approximation error using different types of seasonality and chooses the one with the lowest value of information criterion.
For some more information about the model and its implementation, see the
vignette: vignette("ces","smooth")
Object of class "smooth" is returned. See ces for details.
Ivan Svetunkov, ivan@svetunkov.ru
Svetunkov, I., Kourentzes, N. (February 2015). Complex exponential smoothing. Working Paper of Department of Management Science, Lancaster University 2015:1, 131.
Svetunkov I., Kourentzes N. (2017) Complex Exponential Smoothing for Time Series Forecasting. Not yet published.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  y < ts(rnorm(100,10,3),frequency=12)
# CES with and without holdout
auto.ces(y,h=20,holdout=TRUE)
auto.ces(y,h=20,holdout=FALSE)
# Selection between "none" and "full" seasonalities
auto.ces(AirPassengers,h=8,holdout=TRUE,
models=c("n","f"),interval="p",level=0.8,ic="AIC")
ourModel < auto.ces(AirPassengers,interval="sp")
summary(ourModel)
forecast(ourModel)
plot(forecast(ourModel))

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.