predict.HoltWinters            package:ts            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:

     predict(object, n.ahead=1, prediction.interval = FALSE,
                         quantile = qnorm(0.975), ...)

_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

quantile: Quantile used for each one-sided prediction interval
          (default: the 0.975 quantile to give a two-sided 0.95
          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)

