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