On the ergodicity and stationarity of the ARMA (1,1) recurrent neural network process

Trapletti, Adrian and Leisch, Friedrich and Hornik, Kurt ORCID: https://orcid.org/0000-0003-4198-9911 (1999) On the ergodicity and stationarity of the ARMA (1,1) recurrent neural network process. Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science", 37. SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, Vienna.

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Abstract

In this note we consider the autoregressive moving average recurrent neural network ARMA-NN(1, 1) process. We show that in contrast to the pure autoregressive process simple ARMA-NN processes exist which are not irreducible. We prove that the controllability of the linear part of the process is sufficient for irreducibility. For the irreducible process essentially the shortcut weight corresponding to the autoregressive part determines whether the overall process is ergodic and stationary.

Item Type: Paper
Keywords: ARMA-Modell / neuronales Netz
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft
Departments > Informationsverarbeitung u Prozessmanag. > Produktionsmanagement > Taudes
Departments > Marketing > Service Marketing und Tourismus
Depositing User: Repository Administrator
Date Deposited: 04 Mar 2002 14:01
Last Modified: 24 Oct 2019 13:41
URI: https://epub.wu.ac.at/id/eprint/652

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