This function is created in order for the package to be compatible with Rob Hyndman's "forecast" package
# S3 method for adam forecast(object, h = 10, newdata = NULL, occurrence = NULL, interval = c("none", "prediction", "confidence", "simulated", "approximate", "semiparametric", "nonparametric", "empirical", "complete"), level = 0.95, side = c("both", "upper", "lower"), cumulative = FALSE, nsim = NULL, ...)
# S3 method for smooth forecast(object, h = 10, interval = c("parametric", "semiparametric", "nonparametric", "none"), level = 0.95, side = c("both", "upper", "lower"), ...)
# S3 method for oes forecast(object, h = 10, interval = c("parametric", "semiparametric", "nonparametric", "none"), level = 0.95, side = c("both", "upper", "lower"), ...)
# S3 method for msdecompose forecast(object, h = 10, interval = c("parametric", "semiparametric", "nonparametric", "none"), level = 0.95, model = NULL, ...)
Time series model for which forecasts are required.
The new data needed in order to produce forecasts.
The vector containing the future occurrence variable (values in [0,1]), if it is known.
What type of mechanism to use for interval construction.
For ADAM: the
recommended option is
interval="prediction", which will use analytical
solutions for pure additive models and simulations for the others.
interval="simulated" is the slowest method, but is robust to the type of
analytical formulae for conditional h-steps ahead variance, but is approximate
for the non-additive error models.
interval="semiparametric" relies on the
multiple steps ahead forecast error (extracted via
rmultistep method) and on
the assumed distribution of the error term.
Taylor & Bunn (1999) approach with quantile regressions.
constructs intervals based on empirical quantiles of multistep forecast errors.
interval="complete" will call for
reforecast() function and produce
interval based on the uncertainty around the parameters of the model.
interval="confidence" tries to generate the confidence intervals
for the point forecast based on the
For es, ssarima etc, see the description in es.
Confidence level. Defines width of prediction interval.
Defines, whether to provide
"both" sides of prediction
interval or only
TRUE, then the cumulative forecast and prediction
interval are produced instead of the normal ones. This is useful for
inventory control systems.
Number of iterations to do in case of
The type of ETS model to fit on the decomposed trend. Only applicable to
"msdecompose" class. This is then returned in parameter "esmodel". If
it will be selected automatically based on the type of the used decomposition (either
among pure additive or among pure additive ETS models).
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).
mean - point forecasts of the model
lower - lower bound of prediction interval.
upper - upper bound of prediction interval.
level - confidence level.
interval - binary variable (whether interval were produced or not).
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, Springer-Verlag.