AI RESEARCH
ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
arXiv CS.LG
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ArXi:2605.02692v1 Announce Type: cross 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