NNET                  package:tsDyn                  R Documentation

_N_e_u_r_a_l _N_e_t_w_o_r_k _n_o_n_l_i_n_e_a_r _a_u_t_o_r_e_g_r_e_s_s_i_v_e _m_o_d_e_l

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

     Neural Network nonlinear autoregressive model.

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

     nnetTs(x, m, d = 1, steps = d, series, size, 
             control = list(trace = FALSE))

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

       x: time series 

m, d, steps: embedding dimension, time delay, forecasting steps 

  series: time series name (optional) 

    size: number of hidden units in the neural network

 control: control list to be passed to 'nnet::nnet' optimizer

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

     Neural network model with 1 hidden layer and linear output:


 x[t+steps] = beta[0] + sum_j beta[j] g( gamma[0,j] + sum_i gamma[i,j] x[t-(i-1) d] )


     Model is estimated using the 'nnet' function in 'nnet' package.
     Optimization is done via the BFGS method of 'optim'. Note that for
     this model, no additional model-specific summary and plot methods
     are made available from this package.

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

     An object of class 'nlar', subclass 'nnetTs', i.e. a list with
     mostly 'nnet::nnet' internal structures.

_A_u_t_h_o_r(_s):

     Antonio, Fabio Di Narzo

_R_e_f_e_r_e_n_c_e_s:

     Non-linear time series models in empirical finance, Philip Hans
     Franses and Dick van Dijk, Cambridge: Cambridge University Press
     (2000).

     Non-Linear Time Series: A Dynamical Systems Approach, Tong, H.,
     Oxford: Oxford University Press (1990).

     Chaos: A Statistical Perspective, Chan, K., Tong, H., New York:
     Springer-Verlag (2001).

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

     #fit a Neural Network model
     mod.nnet <- nnetTs(log(lynx), m=2, size=3)
     mod.nnet

