smooth (version 2.6.0)

forecast.smooth: Forecasting time series using smooth functions

Description

This function is created in order for the package to be compatible with Rob Hyndman's "forecast" package

Usage

# 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,
  ...)

Arguments

object

Time series model for which forecasts are required.

h

Forecast horizon

interval

Type of interval to construct. See es for details.

level

Confidence level. Defines width of prediction interval.

side

Defines, whether to provide "both" sides of prediction interval or only "upper", or "lower".

...

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 NULL, then it will be selected automatically based on the type of the used decomposition (either among pure additive or among pure additive ETS models).

Value

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).

Details

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!

References

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.

See Also

ets, forecast

Examples

# NOT RUN {
ourModel <- ces(rnorm(100,0,1),h=10)

forecast.smooth(ourModel,h=10)
forecast.smooth(ourModel,h=10,interval=TRUE)
plot(forecast.smooth(ourModel,h=10,interval=TRUE))

# }

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