A statistical model is autoregressive if it predicts future values based on past values (i.e., predicting future stock prices based on past performance).

The autoencoder network model for HIV classification, proposed in this paper, thus outperforms the conventional feedforward neural network models and is a much better classifier.

We consider a p-th order autoregressive process with autoregressive conditionally heteroscedastic (ARCH) errors. A series representation and some ergodic properties of the first order ARCH errors are ...