Forecasting time series

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.

Usage

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

# Default S3 method
forecast(object, ...)

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

Arguments

object: a time series or time series model for which forecasts are required.
h: Number of periods for forecasting. Default value is twice the largest seasonal period (for seasonal data) or ten (for non-seasonal data).
level: Confidence levels for prediction intervals.
fan: If TRUE, level is set to seq(51, 99, by = 3). This is suitable for fan plots.
robust: If TRUE, the function is robust to missing values and outliers in object. This argument is only valid when object is of class ts.
lambda: 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.
biasadj: 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.
find.frequency: If TRUE, the function determines the appropriate period, if the data is of unknown period.
allow.multiplicative.trend: If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted.
model: An object describing a time series model; e.g., one of of class ets, Arima, bats, bats, 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.
x: a numeric vector or time series of class ts.

Value

An object of class forecast.

Details

For example, the function forecast.Arima() makes forecasts based on the results produced by stats::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.

forecast class

An object of class forecast is a list usually containing at least the following elements:

model: A list containing information about the fitted model
method: The name of the forecasting method as a character string
mean: Point forecasts as a time series
lower: Lower limits for prediction intervals
upper: Upper limits for prediction intervals
level: The confidence values associated with the prediction intervals
x: The original time series.
residuals: Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.
fitted: Fitted values (one-step forecasts)

The function summary can be used to obtain and print a summary of the results, while the functions plot and autoplot produce plots of the forecasts and prediction intervals. The generic accessors functions fitted.values and residuals extract various useful features from the underlying model.

Author

Rob J Hyndman

Examples


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.