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In this paper, we use convolutional neural networks to address the problem ofmodel identification for autoregressive moving average time series models. Wecompare the performance of several neural network architectures, trained onsimulated time series, with likelihood based methods, in particular the Akaikeand Bayesian information criteria. We find that our neural networks cansignificantly outperform these likelihood based methods in terms of accuracyand, by orders of magnitude, in terms of speed.

Model identification for ARMA time series through convolutional neural networks