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, ... )
Model specification (see "Specials" section).
The optimization criterion. Defaults to the log-likelihood
Type of parameter space to impose:
The information criterion used in selecting the model.
If TRUE (default), the models with infinite variance will not be allowed. These restricted model components are AMM, AAM, AMA, and MMA.
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 M3-competition 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.
error special is used to specify the form of the error term.
error(method = c("A", "M"))
|The form of the error term: either additive ("A") or multiplicative ("M"). If the error is multiplicative, the data must be non-negative. All specified methods are tested on the data, and the one that gives the best fit (lowest |
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(1e-04, 0.9999), beta = NULL, beta_range = c(1e-04, 0.9999), phi = NULL, phi_range = c(0.8, 0.98))
|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 |
|The value of the smoothing parameter for the level. If |
|The value of the smoothing parameter for the slope. If |
|The value of the dampening parameter for the slope. If |
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").
|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 |
|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").|
|The value of the smoothing parameter for the seasonal pattern. If |
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), 439--454.
Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The admissible parameter space for exponential smoothing models". Annals of Statistical Mathematics, 60(2), 407--426.
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, Springer-Verlag. http://www.exponentialsmoothing.net.
#> # A mable: 1 x 1 #> `ETS(log(value) ~ season("A"))` #> <model> #> 1 <ETS(A,A,A)>