On the stationarity of autoregressive neural network models

Friedrich Leisch, Adrian Trapletti, Kurt Hornik

Publikation: Working/Discussion PaperWU Working Paper

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.
OriginalspracheEnglisch
ErscheinungsortVienna
HerausgeberSFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business
PublikationsstatusVeröffentlicht - 1998

Publikationsreihe

NameReport Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Nr.21

WU Working Paper Reihe

  • Report Series SFB \Adaptive Information Systems and Modelling in Economics and Management Science\

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