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 nlar

References

FUNFITS manual

See Also

lle, predict.nlar

Examples

# 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


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