predict.HoltWinters          package:stats          R Documentation

_p_r_e_d_i_c_t_i_o_n _f_u_n_c_t_i_o_n _f_o_r _f_i_t_t_e_d _H_o_l_t-_W_i_n_t_e_r_s _m_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computes predictions and prediction intervals for models fitted by
     the Holt-Winters method.

_U_s_a_g_e:

     ## S3 method for class 'HoltWinters':
     predict(object, n.ahead=1, prediction.interval = FALSE,
                         level = 0.95, ...)

_A_r_g_u_m_e_n_t_s:

  object: An object of class 'HoltWinters'.

 n.ahead: Number of future periods to predict.

prediction.interval: logical. If 'TRUE', the lower and upper bounds of
          the corresponding prediction intervals are computed.

   level: Confidence level for the prediction interval.

     ...: arguments passed to or from other methods.

_V_a_l_u_e:

     A time series of the predicted values. If prediction intervals are
     requested, a multiple time series is returned with columns 'fit',
     'lwr' and 'upr' for the predicted values and the lower and upper
     bounds respectively.

_A_u_t_h_o_r(_s):

     David Meyer david.meyer@ci.tuwien.ac.at

_R_e_f_e_r_e_n_c_e_s:

     C.C Holt (1957) Forecasting seasonals and trends by exponentially
     weighted moving averages, ONR Research Memorandum, Carnigie
     Institute 52.

     P.R Winters (1960) Forecasting sales by exponentially weighted
     moving averages, _Management Science_ *6*, 324-342.

_S_e_e _A_l_s_o:

     'HoltWinters'

_E_x_a_m_p_l_e_s:

     data(co2)
     m <- HoltWinters(co2)
     p <- predict(m, 50, prediction.interval = TRUE)
     plot(m, p)

