AC-GATE is a lag-gated neural encoder that conditions lag-weight distributions on entity proxies to recover heterogeneous lags as structural model outputs in panel time series.
A comprehensive sur- vey on statistical and deep learning models for panel data analysis: A comprehensive survey on statistical and deep learning
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Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series
AC-GATE is a lag-gated neural encoder that conditions lag-weight distributions on entity proxies to recover heterogeneous lags as structural model outputs in panel time series.