forecast.smooth: Forecasting time series using smooth functions

In smooth: Forecasting Using State Space Models

Forecasting time series using smooth functions

Description

Function produces conditional expectation (point forecasts) and prediction intervals for the estimated model.

Usage

## S3 method for class '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, scenarios = FALSE, ...)

## 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, ...)

## S3 method for class '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.
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	What type of mechanism to use for interval construction. 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 model. interval="approximate" (aka interval="parametric") uses 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. interval="nonparametric" uses Taylor & Bunn (1999) approach with quantile regressions. interval="empirical" 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. Finally, interval="confidence" tries to generate the confidence intervals for the point forecast based on the reforecast method.
level	Confidence level. Defines width of prediction interval.
side	Defines, whether to provide "both" sides of prediction interval or only "upper", or "lower".
cumulative	If TRUE, then the cumulative forecast and prediction interval are produced instead of the normal ones. This is useful for inventory control systems.
nsim	Number of iterations to do in cases of interval="simulated", interval="prediction" (for mixed and multiplicative model), interval="confidence" and interval="complete". The default value for the prediction / simulated interval is 1000. In case of confidence or complete intervals, this is set to 100.
scenarios	Binary, defining whether to return scenarios produced via simulations or not. Only works if interval="simulated". If TRUE the object will contain scenarios variable.
...	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 multiplicative ETS models).

Details

By default the function will generate conditional expectations from the estimated model and will also produce a variety of prediction intervals based on user preferences.

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).

• scenarios - in case of forecast.adam() and interval="simulated" returns matrix with scenarios (future paths) that were used in simulations.

Author(s)

Ivan Svetunkov, [e-mail]

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.

See Also

forecast

Examples

ourModel <- ces(rnorm(100,0,1),h=10)

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

smooth documentation built on Oct. 1, 2024, 5:07 p.m.