Returns forecasts and other information for exponential smoothing forecasts
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, ...)
a numeric vector or time series of class
Number of periods for forecasting.
Confidence level for prediction intervals.
If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
Method used for selecting initial state values. If
Value of smoothing parameter for the level. If
Box-Cox transformation parameter. If
Use adjusted back-transformed mean for Box-Cox 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.
Deprecated. Included for backwards compatibility.
Other arguments passed to
If TRUE, use a damped trend.
If TRUE, an exponential trend is fitted. Otherwise, the trend is (locally) linear.
Value of smoothing parameter for the trend. If
Value of damping parameter if
Type of seasonality in
Value of smoothing parameter for the seasonal component. If
An object of class "
summary is used to obtain and print a summary of the
results, while the function
plot produces a plot of the forecasts and
The generic accessor functions
extract useful features of the value returned by
ets and associated
An object of class
"forecast" is a list containing at least the
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
object itself or the time series used to create the model
Residuals from the fitted model.
Fitted values (one-step forecasts)
ses, holt and hw are simply convenient wrapper functions for
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 and Athanasopoulos (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. https://OTexts.org/fpp2/
fcast <- holt(airmiles) plot(fcast)deaths.fcast <- hw(USAccDeaths,h=48) plot(deaths.fcast)