ETS.Rd
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. 
...  Other arguments 
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.)
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"). 
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").  
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 level will 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  . 
season
special is used to specify the form of the seasonal term and associated parameters.
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").  
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.
HoltWinters
, RW
,
ARIMA
.
#> # A mable: 1 x 1 #> `ETS(log(value) ~ season("A"))` #> <model> #> 1 <ETS(A,A,A)>