Description Usage Arguments Details Value Author(s) References See Also Examples
This function is created in order for the package to be compatible with Rob Hyndman's "forecast" package
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## S3 method for class 'adam'
forecast(object, h = 10, newdata = NULL,
occurrence = NULL, interval = c("none", "prediction", "confidence",
"simulated", "approximate", "semiparametric", "nonparametric"),
level = 0.95, side = c("both", "upper", "lower"), cumulative = FALSE,
nsim = 10000, ...)
## S3 method for class 'smooth'
forecast(object, h = 10, interval = c("parametric",
"semiparametric", "nonparametric", "none"), level = 0.95,
side = c("both", "upper", "lower"), ...)
## S3 method for class 'oes'
forecast(object, h = 10, interval = c("parametric",
"semiparametric", "nonparametric", "none"), level = 0.95,
side = c("both", "upper", "lower"), ...)
## S3 method for class 'msdecompose'
forecast(object, h = 10, interval = c("parametric",
"semiparametric", "nonparametric", "none"), level = 0.95, model = NULL,
...)

object 
Time series model for which forecasts are required. 
h 
Forecast horizon. 
newdata 
The new data needed in order to produce forecasts. 
occurrence 
The vector containing the future occurrence variable (values in [0,1]), if it is known. 
interval 
Type of interval to construct. See es for details. 
level 
Confidence level. Defines width of prediction interval. 
side 
Defines, whether to provide 
cumulative 
If 
nsim 
Number of iterations to do in case of 
... 
Other arguments accepted by either es, ces, gum or ssarima. 
model 
The type of ETS model to fit on the decomposed trend. Only applicable to
"msdecompose" class. This is then returned in parameter "esmodel". If 
This is not a compulsory function. You can simply use es,
ces, gum or ssarima without
forecast.smooth
. But if you are really used to forecast
function, then go ahead!
Returns object of class "smooth.forecast", which contains:
model
 the estimated model (ES / CES / GUM / SSARIMA).
method
 the name of the estimated model (ES / CES / GUM / SSARIMA).
forecast
aka mean
 point forecasts of the model
(conditional mean).
lower
 lower bound of prediction interval.
upper
 upper bound of prediction interval.
level
 confidence level.
interval
 binary variable (whether interval were produced or not).
Ivan Svetunkov, ivan@svetunkov.ru
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, SpringerVerlag.
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