On the stationarity of autoregressive neural network models

Abstract

We analyze the asymptotic behavior of autoregressive neural network (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 linear 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.

Publication
In Proceedings of CoWAN 98, Cottbuser Workshop Aspekte Neuronalen Lernens, Shaker Verlag
Adrian Trapletti
Adrian Trapletti
PhD, CEO

Quant, software engineer, and consultant mostly investment industry. Long-term contributor and package author R Project for Statistical Computing.

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