Returns ETS model specified by the formula.
ETS( formula, opt_crit = c("lik", "amse", "mse", "sigma", "mae"), nmse = 3, bounds = c("both", "usual", "admissible"), ic = c("aicc", "aic", "bic"), restrict = TRUE, ... )
formula  Model specification (see "Specials" section). 

opt_crit  The optimization criterion. Defaults to the loglikelihood

nmse  If 
bounds  Type of parameter space to impose: 
ic  The information criterion used in selecting the model. 
restrict  If TRUE (default), the models with infinite variance will not be allowed. These restricted model components are AMM, AAM, AMA, and MMA. 
...  Other arguments 
A model specification.
Based on the classification of methods as described in Hyndman et al (2008).
The methodology is fully automatic. The model is chosen automatically if not specified. This methodology performed extremely well on the M3competition data. (See Hyndman, et al, 2002, below.)
The specials define the methods and parameters for the components (error, trend, and seasonality) of an ETS model. If more than one method is specified, ETS
will consider all combinations of the specified models and select the model which best fits the data (minimising ic
). The method argument for each specials have reasonable defaults, so if a component is not specified an appropriate method will be chosen automatically.
There are a couple of limitations to note about ETS models:
It does not support exogenous regressors.
It does not support missing values. You can complete missing values in the data with imputed values (e.g. with tidyr::fill()
, or by fitting a different model type and then calling fabletools::interpolate()
) before fitting the model.
The error
special is used to specify the form of the error term.
error(method = c("A", "M"))
method  The form of the error term: either additive ("A") or multiplicative ("M"). If the error is multiplicative, the data must be nonnegative. All specified methods are tested on the data, and the one that gives the best fit (lowest ic ) will be kept. 
The trend
special is used to specify the form of the trend term and associated parameters.
trend(method = c("N", "A", "Ad"), alpha = NULL, alpha_range = c(1e04, 0.9999), beta = NULL, beta_range = c(1e04, 0.9999), phi = NULL, phi_range = c(0.8, 0.98))
method  The form of the trend term: either none ("N"), additive ("A"), multiplicative ("M") or damped variants ("Ad", "Md"). All specified methods are tested on the data, and the one that gives the best fit (lowest ic ) will be kept. 
alpha  The value of the smoothing parameter for the level. If alpha = 0 , the level will not change over time. Conversely, if alpha = 1 the level will update similarly to a random walk process. 
alpha_range  If alpha=NULL , alpha_range provides bounds for the optimised value of alpha . 
beta  The value of the smoothing parameter for the slope. If beta = 0 , the slope will not change over time. Conversely, if beta = 1 the slope will have no memory of past slopes. 
beta_range  If beta=NULL , beta_range provides bounds for the optimised value of beta . 
phi  The value of the dampening parameter for the slope. If phi = 0 , the slope will be dampened immediately (no slope). Conversely, if phi = 1 the slope will not be dampened. 
phi_range  If phi=NULL , phi_range provides bounds for the optimised value of phi . 
The season
special is used to specify the form of the seasonal term and associated parameters. To specify a nonseasonal model you would include season(method = "N")
.
season(method = c("N", "A", "M"), period = NULL, gamma = NULL, gamma_range = c(1e04, 0.9999))
method  The form of the seasonal term: either none ("N"), additive ("A") or multiplicative ("M"). All specified methods are tested on the data, and the one that gives the best fit (lowest ic ) will be kept. 
period  The periodic nature of the seasonality. This can be either a number indicating the number of observations in each seasonal period, or text to indicate the duration of the seasonal window (for example, annual seasonality would be "1 year"). 
gamma  The value of the smoothing parameter for the seasonal pattern. If gamma = 0 , the seasonal pattern will not change over time. Conversely, if gamma = 1 the seasonality will have no memory of past seasonal periods. 
gamma_range  If gamma=NULL , gamma_range provides bounds for the optimised value of gamma . 
Hyndman, R.J., Koehler, A.B., Snyder, R.D., and Grose, S. (2002) "A state space framework for automatic forecasting using exponential smoothing methods", International J. Forecasting, 18(3), 439454.
Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The admissible parameter space for exponential smoothing models". Annals of Statistical Mathematics, 60(2), 407426.
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, SpringerVerlag. http://www.exponentialsmoothing.net.
#> # A mable: 1 x 1 #> `ETS(log(value) ~ season("A"))` #> <model> #> 1 <ETS(A,A,A)>