Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation

This paper proposes a new method for estimating parameters in autoregressive (AR) time series models by using neural networks instead of traditional statistical methods. The key advantage is that the neural network approach is much faster (up to 34x speedup), more reliable (doesn't fail like conventional methods do 55% of the time), and still produces accurate, interpretable results comparable to traditional approaches.