nnet: Neural Network nonlinear autoregressive model

Description Usage Arguments Details Value Author(s) References Examples

Description

Neural Network nonlinear autoregressive model.

Usage

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

Arguments

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

Details

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.

Value

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

Author(s)

Antonio, Fabio Di Narzo

References

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).

Examples

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

Example output

Non linear autoregressive model

NNET time series model
a 2-3-1 network with 13 weights
options were - linear output units 

tsDyn documentation built on Jan. 23, 2018, 1:01 a.m.