ses

0th

Percentile

Exponential smoothing forecasts

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

Keywords
ts
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.
Details

ses, holt and hw are simply convenient wrapper functions for 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:

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.

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 7.3, License: GPL (>= 2)

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