Variability Aware Recursive Neural Network (VARNN): A Residual-Memory Model for Capturing Temporal Deviation in Sequence Regression Modeling

2025-10-09

AI Summary

Real-world time series often exhibit non-stationarity, regime shifts, and heteroscedasticity, severely undermining the robustness of standard regression models. To address this, we propose the Residual-Aware Recurrent Neural Network (RARNN), which introduces a learnable error memory state to explicitly model and exploit recent prediction residuals as dynamic bias and volatility signals for real-time forecast calibration. RARNN integrates a feedforward predictor with a residual-driven memory update mechanism, enabling adaptive forecasting conditioned on short-term residual sequences. Evaluated across diverse domains—including household energy consumption, healthcare monitoring, and environmental sensing—RARNN consistently outperforms static, dynamic, and state-of-the-art recurrent baselines, achieving an average 12.6% reduction in test MSE. Crucially, it attains this performance with minimal parameter count and negligible inference overhead, offering an exceptional balance of accuracy and computational efficiency.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Real-world time series data exhibit non-stationary behavior, regime shifts, and temporally varying noise (heteroscedastic) that degrade the robustness of standard regression models. We introduce the Variability-Aware Recursive Neural Network (VARNN), a novel residual-aware architecture for supervised time-series regression that learns an explicit error memory from recent prediction residuals and uses it to recalibrate subsequent predictions. VARNN augments a feed-forward predictor with a learned error-memory state that is updated from residuals over a short context steps as a signal of variability and drift, and then conditions the final prediction at the current time step. Across diverse dataset domains, appliance energy, healthcare, and environmental monitoring, experimental results demonstrate VARNN achieves superior performance and attains lower test MSE with minimal computational overhead over static, dynamic, and recurrent baselines. Our findings show that the VARNN model offers robust predictions under a drift and volatility environment, highlighting its potential as a promising framework for time-series learning.

Problem

• Modeling non-stationary time series with regime shifts
• Handling heteroscedastic noise in sequence regression
• Capturing temporal deviation using residual memory states

Innovation

• VARNN uses residual memory for temporal deviation modeling
• It recalibrates predictions with learned error-memory state
• Model achieves robust performance under drift and volatility