The methodology is fully automatic. The only required argument for ets is the time series. The model is chosen automatically if not specified.

ets <- function(y, model="ZZZ", damped=NULL, alpha=NULL, beta=NULL, gamma=NULL, phi=NULL, additive.only=FALSE, lambda=NULL, biasadj=FALSE, lower=c(rep(0.0001, 3), 0.8), upper=c(rep(0.9999, 3), 0.98), opt.crit=c("lik", "amse", "mse", "sigma", "mae"), nmse=3, bounds=c("both", "usual", "admissible"), ic=c("aicc", "aic", "bic"), restrict=TRUE, allow.multiplicative.trend=FALSE, use.initial.values=FALSE, na.action = c("na.contiguous", "na.interp", "na.fail"), ...)

• y = a numeric vector or time series of class ts

• model Usually a three-character string identifying method using the framework terminology such as:

  1. The first letter denotes the error type (A, M or Z)
  2. The second denotes the trend type (N,A, M or Z)
  3. the third letter denotes the season type (N,A, M or Z)

  In all cases, N=none, A=additive, M=multiplicative and Z=automatically selected. So, for example, ANN is simple exponential smoothing with additive errors, MAM is multiplicative Holt-Winters' method with multiplicative errors, and so on.

• damped If TRUE, use a damped trend (either additive or multiplicative). If NULL, both damped and non-damped trends will be tried and the best model (according to the information criterion ic) returned.

• alpha Value of alpha. If NULL, it is estimated.

• beta Value of beta. If NULL, it is estimated.

• gamma Value of gamma. If NULL, it is estimated.

• phi Value of phi. If NULL, it is estimated.

• additive.only If TRUE, will only consider additive models. Default is FALSE.

• lambda Box-Cox transformation parameter. If lambda="auto" then a transformation is automatically selected using BoxCox.lambda. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated. When lambda is specified, additive.only is set to TRUE.

• lower Lower bounds for the parameters (alpha, beta, gamma, phi)

• upper Upper bounds for the parameters (alpha, beta, gamma, phi)

• opt.crit Optimization criterion. One of "mse" (Mean Square Error), "amse" (Average MSE over first \code{nmse} forecast horizons), "sigma" (Standard deviation of residuals), "mae" (Mean of absolute residuals), or "lik" (Log-likelihood, the default).

• nmse Number of steps for average multistep MSE (1<=nmse <=30).

• bounds Type of parameter space to impose: "usual" indicates all parameters must lie between specified lower and upper bounds; "admissible" indicates parameters must lie in the admissible space; "both" (default) takes the intersection of these regions.

• ic Information criterion to be used in model selection.

• restrict If TRUE (default), the models with infinite variance will not be allowed.

• allow.multiplicative.trend If TRUE, models with multiplicative trend are allowed when searching for a model. Otherwise, the model space excludes them. This argument is ignored if a multiplicative trend model is explicitly requested (e.g., using model="MMN").

• use.initial.values If TRUE and model is of class "ets", then the initial values in the model are also not re-estimated.

• na.action A function which indicates what should happen when the data contains NA values. By default, the largest contiguous portion of the time-series will be used.

You can install the development version from Github

# install.packages("remotes")
remotes::install_github("ikhwan12/forecast")

library(forecast)
library(ggplot2)

# ETS forecasts
fit <- ets(bss, model = "MNA")
plot(forecast(fit))

#See the prediction result
res = forecast(fit)
res