Model identification for ARMA time series through convolutional neural networks
This addresses model identification for time series analysis, offering a faster and more accurate alternative to traditional methods, though it appears incremental as it applies existing neural network techniques to a specific domain.
The paper tackled the problem of model identification for ARMA time series by using convolutional neural networks, finding that they significantly outperformed likelihood-based methods like AIC and BIC in accuracy and speed by orders of magnitude.
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.