decompose               package:stats               R Documentation

_C_l_a_s_s_i_c_a_l _S_e_a_s_o_n_a_l _D_e_c_o_m_p_o_s_i_t_i_o_n _b_y _M_o_v_i_n_g _A_v_e_r_a_g_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Decompose a time series into seasonal, trend and irregular
     components using moving averages.  Deals with additive or
     multiplicative seasonal component.

_U_s_a_g_e:

     decompose(x, type = c("additive", "multiplicative"), filter = NULL)

_A_r_g_u_m_e_n_t_s:

       x: A time series.

    type: The type of seasonal component.

  filter: A vector of filter coefficients in reverse time order (as for
          AR or MA coefficients), used for filtering out the seasonal
          component. If 'NULL', a moving average with symmetric window
          is performed.

_D_e_t_a_i_l_s:

     The additive model used is:

     Y[t] = T[t] + S[t] + e[t]

     The multiplicative model used is:

     Y[t] = T[t] * S[t] + e[t]

_V_a_l_u_e:

     An object of class '"decomposed.ts"' with following components: 

seasonal: The seasonal component (i.e., the repeated seasonal figure)

  figure: The estimated seasonal figure only

   trend: The trend component

  random: The remainder part

    type: The value of 'type'

_N_o_t_e:

     The function 'stl' provides a much more sophisticated
     decomposition.

_A_u_t_h_o_r(_s):

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

_S_e_e _A_l_s_o:

     'stl'

_E_x_a_m_p_l_e_s:

     data(co2)
     m <- decompose(co2)
     m$figure
     plot(m)

