ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
The proliferation of large-scale and structurally complex data has spurred the integration of machine learning methods into statistical modeling. Recurrent neural networks (RNNs), a foundational class of models for time-dependent data, can be viewed as nonlinear extensions of classical autoregressive moving average models. Despite their flexibility and empirical success in machine learning, RNNs often suffer from limited interpretability and slow training, which hinders their use in statistics. This paper proposes the Parallelized RNN (ParaRNN), a novel model composed of multiple small recurrent units. ParaRNN admits an additive representation that decouples recurrent dynamics into interp...
| Authors | Yuxi Cai, Lan Li, Feiqing Huang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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