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.cov(obj)
         forecast(obj, ...)
         forecast(obj, data,  horizon=36,
            conditioning.inputs=NULL, 
            conditioning.inputs.forecasts=NULL, percent=NULL)
         forecast(obj, ...)
         forecast(obj, model, ...)

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

   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.

_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 input.data(data). This will be the
     same as the function l() unless input.data(data) is longer than
     output.data(data) (after NAs are trimmed from each separately).
     Otherwise, if conditioning.inputs is specified it is used for
     input.data(data). It must be a time series matrix or a list of
     time series matrices each of which is used in turn as
     input.data(data). The default above is the same as forecast(model,
     trim.na(data), conditioning.inputs=trim.na(input.data(data)) )
     Otherwise, if conditioning.inputs.forecasts is specified it is
     appended  to input.data(data). It must be a time series   matrix
     or a list of time series matrices each of which is  appended to
     input.data(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 output.data(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:

     `feather.forecasts', `horizon.forecasts'

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

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

