R: Forecasting time series

forecast.mts {forecast} | R Documentation

## Forecasting time series

### Description

`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 class '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:

`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 & Mitchell O'Hara-Wild

### See Also

Other functions which return objects of class `"mforecast"` are `forecast.mlm`, `forecast.varest`.

[Package forecast version 8.12 Index]