`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.

`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, biasadj = FALSE,
find.frequency = FALSE, allow.multiplicative.trend = FALSE,
model = NULL, ...)

object

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

...

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`

.

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.

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`

.

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

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