# forecast.mts

##### Forecasting time series

`mforecast`

is a class of objects for forecasting from multivariate
time series or multivariate time series models. The function invokes
particular *methods* which depend on the class of the first argument.

##### Usage

```
# S3 method for mts
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, ...)
```

##### Arguments

- object
a multivariate time series or multivariate 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`mts`

.- 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.

- ...
Additional arguments affecting the forecasts produced.

##### Details

For example, the function `forecast.mlm`

makes multivariate
forecasts based on the results produced by `tslm`

.

##### Value

An object of class "`mforecast`

".

The function `summary`

is used to obtain and print a summary of the
results, while the function `plot`

produces a plot of the multivariate
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 `"mforecast"`

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 `"mforecast"`

are `forecast.mlm`

, `forecast.varest`

.

*Documentation reproduced from package forecast, version 8.5, License: GPL-3*