nnet: Neural Network nonlinear autoregressive model

In tsDyn: Nonlinear Time Series Models with Regime Switching

Neural Network nonlinear autoregressive model

Description

Neural Network nonlinear autoregressive model.

Usage

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+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.

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

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

tsDyn documentation built on Oct. 31, 2024, 5:08 p.m.