Nonlinear autoregressive model
Usage
nlar(Y, lags, cov=NA, method="nnreg", ...)
Arguments
Y
|
The time series
|
lags
|
A vector that specifies which lags of Y to use in the autoregressive function
|
cov
|
A vector or matrix of covariates as long as the Y series these are
additional variables that will be used in the regression function
|
method
|
Name of S function to fit the nonparametric model e.g. nnreg tps
addreg
|
...
|
Optional argument that as passed through to the regression method
|
Description
his function fits a model of the form:
Y_t = f( Y_(t-l1),...{},Y_(t-ld),S_t) + e_t
Where e_t is assumed to mean zero, uncorrelated errors. Such a form is
useful for testing whether a system is chaotic.Value
An object of class nlarReferences
FUNFITS manualSee Also
lle, predict.nlarExamples
# Fit the rossler series. A toy dynamical system that is chaotic
# Use a neural network with 4 hidden units based on lags 1, 2 and 3 of
the series.
nlar( rossler,lags=c(1,2,3), method="nnreg",k1=4)-> out
summary(out)
plot( out)
lle( out) # calculate local and global Lyapunov exponents