Returns forecasts and other information for exponential smoothing forecasts
applied to y
.
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, phi = 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, phi = NULL, lambda = NULL, biasadj = FALSE, x = y, ... )
y  a numeric vector or time series of class 

h  Number of periods for forecasting. 
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

alpha  Value of smoothing parameter for the level. If 
lambda  BoxCox transformation parameter. If 
biasadj  Use adjusted backtransformed mean for BoxCox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values. 
x  Deprecated. Included for backwards compatibility. 
...  Other arguments passed to 
damped  If TRUE, use a damped trend. 
exponential  If TRUE, an exponential trend is fitted. Otherwise, the trend is (locally) linear. 
beta  Value of smoothing parameter for the trend. If 
phi  Value of damping parameter if 
seasonal  Type of seasonality in 
gamma  Value of smoothing parameter for the seasonal component. If

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:
A list containing information about the fitted model
The name of the forecasting method as a character string
Point forecasts as a time series
Lower limits for prediction intervals
Upper limits for prediction intervals
The confidence values associated with the prediction intervals
The original time series
(either object
itself or the time series used to create the model
stored as object
).
Residuals from the fitted model.
Fitted values (onestep forecasts)
ses, holt and hw are simply convenient wrapper functions for
forecast(ets(...))
.
Hyndman, R.J., Koehler, A.B., Ord, J.K., Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, SpringerVerlag: New York. http://www.exponentialsmoothing.net.
Hyndman and Athanasopoulos (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. https://otexts.com/fpp2/
ets
, HoltWinters
,
rwf
, arima
.
Rob J Hyndman