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forecast is a generic function for forecasting from time series or time series models. The function invokes particular methods which depend on the class of the first argument.


# S3 method for ts
  h = ifelse(frequency(object) > 1, 2 * frequency(object), 10),
  level = c(80, 95),
  fan = FALSE,
  robust = FALSE,
  lambda = NULL,
  biasadj = FALSE,
  find.frequency = FALSE,
  allow.multiplicative.trend = FALSE,
  model = NULL,

# S3 method for default
forecast(object, ...)

# S3 method for forecast
print(x, ...)



a time series or time series model for which forecasts are required


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.


If TRUE, the function is robust to missing values and outliers in object. This argument is only valid when object is of class ts.


Box-Cox transformation parameter. If lambda="auto", then a transformation is automatically selected using BoxCox.lambda. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.


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.


If TRUE, the function determines the appropriate period, if the data is of unknown period.


If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted.


An object describing a time series model; e.g., one of of class ets, Arima, bats, tbats, or nnetar.


Additional arguments affecting the forecasts produced. If model=NULL, forecast.ts passes these to ets or stlf depending on the frequency of the time series. If model is not NULL, the arguments are passed to the relevant modelling function.


a numeric vector or time series of class ts.


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 accessors functions fitted.values and residuals

extract various useful features of the value returned by forecast$model.

An object of class "forecast" is a list usually 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. For models with additive errors, the residuals will be x minus the fitted values.


Fitted values (one-step forecasts)


For example, the function forecast.Arima makes forecasts based on the results produced by arima.

If model=NULL,the function forecast.ts makes forecasts using ets models (if the data are non-seasonal or the seasonal period is 12 or less) or stlf (if the seasonal period is 13 or more).

If model is not NULL, forecast.ts will apply the model to the object time series, and then generate forecasts accordingly.

See also

Other functions which return objects of class "forecast" are forecast.ets, forecast.Arima, forecast.HoltWinters, forecast.StructTS, meanf, rwf, splinef, thetaf, croston, ses, holt, hw.


Rob J Hyndman


WWWusage %>% forecast %>% plot

fit <- ets(window(WWWusage, end=60))
fc <- forecast(WWWusage, model=fit)
#> Model is being refit with current smoothing parameters but initial states are being re-estimated.
#> Set 'use.initial.values=TRUE' if you want to re-use existing initial values.