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VARNN introduces a novel residual-memory mechanism that explicitly models structured prediction errors to handle non-stationarity in time-series regression tasks. By mapping scalar innovations into a learned nonlinear representation, the architecture outperforms static baselines across diverse domains like energy and healthcare without relying on external AI hype. This work advances fundamental sequence modeling techniques by demonstrating how leveraging local temporal mismatches can improve generalization in complex, real-world data environments.
Variability Aware Recursive Neural Network (VARNN): A Residual-Memory Model for Capturing Temporal Deviation in Sequence Regression Modeling
arXiv:2510.08944v2 Announce Type: replace Abstract: Real-world time-series regression often involves non-stationarity, heteroscedasticity, and regime changes, under which recent prediction errors may contain structured information about local temporal mismatch between model predictions and observations. Learning how to represent and reuse these errors can therefore provide useful information for subsequent prediction. We introduce the Variability-Aware Recursive Neural Network (VARNN), a residual-aware architecture for supervised time-series regression that learns an explicit residual-memory state from recent prediction errors and uses it to condition subsequent predictions. Specifically, VARNN maps scalar prediction innovations into a learned nonlinear, vector-valued residual representation over a short context. Across nine datasets spanning energy, healthcare, and environmental domains, VARNN achieves lower test MSE than the compared static, lag-based, and sequence-model baselines. Targeted ablations further show that learned projected residual memory improves predictive accuracy over direct scalar residual feedback, supporting the benefit of a learned nonlinear representation of prediction deviations.
Abstract:Real-world time-series regression often involves non-stationarity, heteroscedasticity, and regime changes, under which recent prediction errors may contain structured information about local temporal mismatch between model predictions and observations. Learning how to represent and reuse these errors can therefore provide useful information for subsequent prediction. We introduce the Variability-Aware Recursive Neural Network (VARNN), a residual-aware architecture for supervised time-series regression that learns an explicit residual-memory state from recent prediction errors and uses it to condition subsequent predictions. Specifically, VARNN maps scalar prediction innovations into a learned nonlinear, vector-valued residual representation over a short context. Across nine datasets spanning energy, healthcare, and environmental domains, VARNN achieves lower test MSE than the compared static, lag-based, and sequence-model baselines. Targeted ablations further show that learned projected residual memory improves predictive accuracy over direct scalar residual feedback, supporting the benefit of a learned nonlinear representation of prediction deviations.
Machine Learning (cs.LG)
arXiv:2510.08944 [cs.LG]
(or arXiv:2510.08944v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2510.08944 Focus to learn more arXiv-issued DOI via DataCite
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