NNET | R Documentation |
Neural Network nonlinear autoregressive model.
nnetTs(x, m, d = 1, steps = d, series, size,
control = list(trace = FALSE))
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 |
Neural network model with 1 hidden layer and linear output:
x_{t+s} = \beta_0 + \sum_{j=1}^D \beta_j g( \gamma_{0j} +
\sum_{i=1}^{m} \gamma_{ij} 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.
An object of class nlar
, subclass nnetTs
, i.e. a list
with mostly nnet::nnet
internal structures.
Antonio, Fabio Di Narzo
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).
#fit a Neural Network model
mod.nnet <- nnetTs(log(lynx), m=2, size=3)
mod.nnet
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