forecast: forecast – R documentation

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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

forecast(object, ...)

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

## S3 method for class 'ts'
forecast(
  object,
  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,
  ...
)

Arguments

`object`	a time series or time series model for which forecasts are required
`...`	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.
`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.
`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`, `tbats`, or `nnetar`.

Details

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.

Value

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:

`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 (either `object` itself or the time series used to create the model stored as `object`).
`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)

Author(s)

Rob J Hyndman

Examples

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

forecast

Forecasting Functions for Time Series and Linear Models

v8.14

GPL-3

Authors

Rob Hyndman [aut, cre, cph] (<https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [aut], Christoph Bergmeir [aut] (<https://orcid.org/0000-0002-3665-9021>), Gabriel Caceres [aut], Leanne Chhay [aut], Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>), Fotios Petropoulos [aut] (<https://orcid.org/0000-0003-3039-4955>), Slava Razbash [aut], Earo Wang [aut], Farah Yasmeen [aut] (<https://orcid.org/0000-0002-1479-5401>), R Core Team [ctb, cph], Ross Ihaka [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang [ctb] (<https://orcid.org/0000-0001-5243-233X>), Zhenyu Zhou [ctb]

Initial release

forecast

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

forecast

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