featherForecasts {dse2} | R Documentation

## Multiple Horizon-Step Ahead Forecasts

### Description

Calculate multiple horizon-step ahead forecasts.

### Usage

        featherForecasts(obj, ...)
        ## S3 method for class 'TSestModel':
        featherForecasts(obj, data=NULL, ...)
        ## S3 method for class 'TSdata':
        featherForecasts(obj, model, ...)
        ## S3 method for class 'TSmodel':
        featherForecasts(obj, data, horizon=36,
                 from.periods =NULL, ...)
        is.featherForecasts(obj)

### Arguments

`obj` | an object of class TSmodel.
`data` | an object of class TSdata.
`model` | an object of class TSmodel.
`from.periods` | the starting points to use for forecasts.
`horizon` | the number of periods to forecast.
`...` | for a TSmodel additional arguments are passed to `l()`

### Details

Calculate multiple horizon-step ahead forecasts ie. use the samples indicated by from.periods to calculate forecasts for horizon periods. Thus, for example, the result of featherForecasts(model, data, from.periods=c(200,250,300)) would be forecasts for 1 through 36 steps ahead (the default), starting at the 200th,250th, and 300th point of outputData(data). This function assumes that inputData(data) (the exogenous variable) is as long as necessary for the most future forecast.

### Value

The result is a list of class featherForecasts with elements `model` (a TSestModel), `data`, `from.periods`, `featherForecasts`. The element `featherForecasts` is a list with length(from.periods) elements, each of which is a tframed matrix. There is a plot method for this class.

### See Also

`forecast`, `horizonForecasts`

### Examples

    if(is.R()) data("egJofF.1dec93.data", package="dse1")
    model <- estVARXls(egJofF.1dec93.data)
    pr <- featherForecasts(model, egJofF.1dec93.data)
    tfplot(pr)

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