forecast.ts: Forecasting time series

In forecast: Forecasting Functions for Time Series and Linear Models

Forecasting time series

Description

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 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.
x	a numeric vector or time series of class ts.

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.

Value

An object of class forecast.

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 accessor functions fitted.values and residuals extract various useful features from the underlying model.

Author(s)

Rob J Hyndman

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().

Examples

WWWusage |> forecast() |> plot()
fit <- ets(window(WWWusage, end = 60))
fc <- forecast(WWWusage, model = fit)

forecast documentation built on March 18, 2026, 9:07 a.m.