ses

0th

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Exponential smoothing forecasts

Returns forecasts and other information for exponential smoothing forecasts applied to x.

Keywords
ts
Usage
ses(x, h=10, level=c(80,95), fan=FALSE, 
    initial=c("optimal","simple"), alpha=NULL, ...)
holt(x, h=10, damped=FALSE, level=c(80,95), fan=FALSE, 
    initial=c("optimal","simple"), exponential=FALSE,
    alpha=NULL, beta=NULL, ...)
hw(x, h=2*frequency(x), seasonal="additive", damped=FALSE, 
    level=c(80,95), fan=FALSE, initial=c("optimal","simple"), 
    exponential=FALSE, alpha=NULL, beta=NULL, gamma=NULL, ...)
Arguments
x
a numeric vector or time series
h
Number of periods for forecasting.
damped
If TRUE, use a damped trend.
seasonal
Type of seasonality in hw model. "additive" or "multiplicative"
level
Confidence level for prediction intervals.
fan
If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
initial
Method used for selecting initial state values. If optimal, the initial values are optimized along with the smoothing parameters using ets. If simple, the initial values are set to val
exponential
If TRUE, an exponential trend is fitted. Otherwise, the trend is (locally) linear.
alpha
Value of smoothing parameter for the level. If NULL, it will be estimated.
beta
Value of smoothing parameter for the trend. If NULL, it will be estimated.
gamma
Value of smoothing parameter for the seasonal component. If NULL, it will be estimated.
...
Other arguments passed to forecast.ets.
Details

ses, holt and hw are simply convenient wrapper functions for forecast(ets(...)).

Value

  • An object of class "forecast".

    The function summary is used to obtain and print a summary of the results, while the function plot produces a plot of the forecasts and prediction intervals.

    The generic accessor functions fitted.values and residuals extract useful features of the value returned by ets and associated functions.

    An object of class "forecast" is a list containing at least the following elements:

  • modelA list containing information about the fitted model
  • methodThe name of the forecasting method as a character string
  • meanPoint forecasts as a time series
  • lowerLower limits for prediction intervals
  • upperUpper limits for prediction intervals
  • levelThe confidence values associated with the prediction intervals
  • xThe original time series (either object itself or the time series used to create the model stored as object).
  • residualsResiduals from the fitted model. That is x minus fitted values.
  • fittedFitted values (one-step forecasts)

References

Hyndman, R.J., Koehler, A.B., Ord, J.K., Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, Springer-Verlag: New York. http://www.exponentialsmoothing.net.

Hyndman, R.J., Athanasopoulos (2012) Forecasting: principles and practice, OTexts: Melbourne, Australia. http://otexts.com/fpp.

See Also

ets, HoltWinters, rwf, arima.

Aliases
  • ses
  • holt
  • hw
Examples
fcast <- holt(airmiles)
plot(fcast)
deaths.fcast <- hw(USAccDeaths,h=48)
plot(deaths.fcast)
Documentation reproduced from package forecast, version 4.04, License: GPL (>= 2)

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