# forecast

##### 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.

- Keywords
- ts

##### Usage

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

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

- 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:

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)

##### 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

```
# NOT RUN {
WWWusage %>% forecast %>% plot
fit <- ets(window(WWWusage, end=60))
fc <- forecast(WWWusage, model=fit)
# }
```

*Documentation reproduced from package forecast, version 8.2, License: GPL-3*