Description Usage Arguments Details Value Author(s) References Examples

Neural Network nonlinear autoregressive model.

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`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+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.

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).

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tsDyn documentation built on June 4, 2018, 1:04 a.m.

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