  In this paper, we use convolutional neural networks to address the problem of
model identification for autoregressive moving average time series models. We
compare the performance of several neural network architectures, trained on
simulated time series, with likelihood based methods, in particular the Akaike
and Bayesian information criteria. We find that our neural networks can
significantly outperform these likelihood based methods in terms of accuracy
and, by orders of magnitude, in terms of speed.
