Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)
Friedrich Leisch, Adrian Trapletti, Kurt Hornik
We analyze the asymptotic behavior of autoregressive neural net(cid:173) work (AR-NN) processes using techniques from Markov chains and non-linear time series analysis. It is shown that standard AR-NNs without shortcut connections are asymptotically stationary. If lin(cid:173) ear shortcut connections are allowed, only the shortcut weights determine whether the overall system is stationary, hence standard conditions for linear AR processes can be used.