Model identification for ARMA time series through convolutional neural networks

We use convolutional neural networks for model identification in ARMA time series models, where our networks are trained on synthetic data with known ground truths. Comparing the performance of these networks with traditional likelihood-based methods, in particular the Akaike and Bayesian Information Criteria, we are able to show that when it comes to statistical inference on ARMA orders, neural networks can significantly outperform likelihood-based methods in terms of accuracy and, by orders of magnitude, in terms of speed. We also observe improvements in terms of time series forecasting. Our approach shows the feasibility of using artificial neural networks for statistical inference in situations where classical likelihood-based methods are difficult or costly to implement.

Automated summary

By training convolutional neural networks on synthetic data with known ground truths, we found that in ARMA time series models, this approach significantly outperforms traditional likelihood-based methods in terms of accuracy and speed, particularly in statistical inference and time series forecasting. This study demonstrates the feasibility of using artificial neural networks for statistical inference in situations where classical likelihood-based methods are difficult or costly to implement.