Long-range out-of-sample properties of autoregressive neural networks.
We consider already-trained discrete autoregressive neural networks in their most general representations, with the exclusion of time-varying input though, and we provide tight sufficient conditions and elementary proofs for the existence of an attractor, uniqueness, and global convergence. Those conditions can be used as easy-to-check criteria when convergence (or not) of long-range predictions is desirable.
University of Southern Denmark, Department of Business and Economics, 55 DK-5230, Odense M, Denmark. pal@sam.sdu.dk
Patrick
P
Leoni
D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors