Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
Autoregressive (AR) models remain widely used in time series analysis due to their interpretability, but convencional parameter estimation methods can be computationally expensive and prone to convergence issues. This paper proposes a Neural Network (NN) formulation of AR estimation by embedding the autoregressive structure directly into a feedforward NN, enabling coefficient estimation through backpropagation while preserving interpretability. Simulation experiments on 125,000 synthetic AR(p) time series with short-term dependence (1 <= p <= 5) show that the proposed NN-based method consistently recovers model coefficients for all series, while Conditional Maximum Likelihood (CML) fails ...
| Authors | Anaísa Lucena, Ana Martins, Armando J. Pinho et al. |
|---|---|
| Primary category | stat.ML |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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