Variability Aware Recursive Neural Network (VARNN): A Residual-Memory Model for Capturing Temporal Deviation in Sequence Regression Modeling
Read the original on arXiv Machine Learning →The paper introduces the Variability-Aware Recursive Neural Network (VARNN), a residual-aware architecture for supervised time-series regression that learns a nonlinear, vector-valued residual representation from recent prediction errors. VARNN conditions subsequent predictions on this learned residual-memory state, mapping scalar prediction innovations into a short-context representation. Experiments on nine datasets across energy, healthcare, and environmental domains show that VARNN achieves lower test MSE than static, lag-based, and sequence-model baselines, and ablations confirm that the learned residual memory improves predictive accuracy over direct scalar residual feedback.
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