forecast                package:dse2                R Documentation

_F_o_r_e_c_a_s_t _M_u_l_t_i_p_l_e _S_t_e_p_s _A_h_e_a_d

_D_e_s_c_r_i_p_t_i_o_n:

     Calculate forecasts multiple steps ahead.

_U_s_a_g_e:

         is.forecast(obj)
         forecast(obj, ...)
         ## S3 method for class 'TSmodel':
         forecast(obj, data,  horizon=36,
            conditioning.inputs=NULL, 
            conditioning.inputs.forecasts=NULL, percent=NULL, ...)
         ## S3 method for class 'TSestModel':
         forecast(obj, ...)
         ## S3 method for class 'TSdata':
         forecast(obj, model, ...)

_A_r_g_u_m_e_n_t_s:

     obj: An object of a class for which a specific method is
          available.

   model: An object of class TSmodel.

    data: An object of class TSdata.

conditioning.inputs: A time series matrix or list of time series
          matrices to use as input variables.

conditioning.inputs.forecasts: A time series matrix or list of time
          series matrices to append to input variables for the forecast
          periods.

horizon : The number of periods to forecast.

 percent: A vector indication percentages of the last input to use for 
          forecast periods.

     ...: arguments passed to other methods.

_D_e_t_a_i_l_s:

     Calculate (multiple) forecasts from the end of data to a horizon
     determined either from supplied input data or the argument horizon
     (more details below). In  the case of a model with no inputs the
     horizon is determined by the argument horizon. In the case of
     models with inputs, on which the forecasts are conditioned, the
     argument horizon is ignored (except when percent is specified) and
     the actual horizon is determined by the inputs in the  following
     way: If inputs are not specified by optional arguments (as below)
     then the default will be to use inputData(data). This will be the
     same as the function l() unless inputData(data) is longer than
     outputData(data) (after NAs are trimmed from each separately).
     Otherwise, if conditioning.inputs is specified it is used for
     inputData(data). It must be a time series matrix or a list of time
     series matrices each of which is used in turn as inputData(data).
     The default above is the same as forecast(model, trimNA(data),
     conditioning.inputs=trimNA(inputData(data)) ) Otherwise, if
     conditioning.inputs.forecasts is specified it is appended  to
     inputData(data). It must be a time series   matrix or a list of
     time series matrices each of which is  appended to inputData(data)
     and the concatenation used as conditioning.inputs. Both
     conditioning.inputs and conditioning.inputs.forecasts should not
     be specified. Otherwise, if percent is specified then
     conditioning.inputs.forecasts are set to percent/100 times the
     value of input corresponding to the last period of
     outputData(data) and used for horizon periods. percent can be a
     vector,  in which case each value is applied in turn. ie
     c(90,100,110) would would  give results for
     conditioning.input.forecasts 10 percent above and below  the last
     value of input.

_V_a_l_u_e:

     The result is an object of class forecast which is a list with 
     elements 'model', 'horizon', 'conditioning.inputs',  'percent',
     'pred' and 'forecast'. The element 'forecast' is a list with
     TSdata objects as elements, one for each element in the list
     conditioning.inputs. The element 'pred' contains the one-step
     ahead forecasts for the preiods when output data is available. 
     There is a plot method for this class.

_S_e_e _A_l_s_o:

     'featherForecasts', 'horizonForecasts'

_E_x_a_m_p_l_e_s:

         if(is.R()) data("egJofF.1dec93.data", package="dse1")
         model <- estVARXls(window(egJofF.1dec93.data, end=c(1985,12)))
         pr <- forecast(model, conditioning.inputs=inputData(egJofF.1dec93.data))
         #tfplot(pr) Rbug 0.90.1
         is.forecast(pr)

