  A time series is a sequence of observations taken sequentially in time. The
autoregressive integrated moving average is a class of the model more used for
times series data. However, this class of model has two critical limitations.
It fits well onlyGaussian data with the linear structure of correlation. Here,
I present a new model named as generalized autoregressive neural networks,
GARNN. The GARNN is an extension of the generalized linear model where the mean
marginal depends on the lagged values via the inclusion of the neural network
in the link function. A practical application of the model is shown using a
well-known poliomyelitis case number, originated analyzed by Zeger and Qaqish
(1988),
