forecast (version 7.2)

ses: Exponential smoothing forecasts

Description

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

Usage

ses(y, h=10, level=c(80,95), fan=FALSE,  initial=c("optimal","simple"), alpha=NULL,  lambda=NULL, biasadj=FALSE, x=y, ...)
holt(y, h=10, damped=FALSE, level=c(80,95), fan=FALSE,  initial=c("optimal","simple"), exponential=FALSE, alpha=NULL, beta=NULL, lambda=NULL, biasadj=FALSE, x=y, ...)
hw(y, h=2*frequency(x), seasonal=c("additive","multiplicative"),  damped=FALSE, level=c(80,95), fan=FALSE,  initial=c("optimal","simple"), exponential=FALSE, alpha=NULL, beta=NULL, gamma=NULL,  lambda=NULL, biasadj=FALSE, x=y, ...)

Arguments

y

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(51,99,by=3). 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 values obtained using simple calculations on the first few observations. See Hyndman & Athanasopoulos (2014) for details.

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.

lambda

Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated. When lambda=TRUE, additive.only is set to FALSE.

biasadj

Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.

x

Deprecated. Included for backwards compatibility.

...

Other arguments passed to forecast.ets.

Value

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: is a list containing at least the following elements:

Details

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

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 (2014) Forecasting: principles and practice, OTexts: Melbourne, Australia. http://www.otexts.org/fpp.

See Also

ets, HoltWinters, rwf, arima.

Examples

fcast <- holt(airmiles)
plot(fcast)
deaths.fcast <- hw(USAccDeaths,h=48)
plot(deaths.fcast)

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