Adaptive Post-Prediction Stabilization for Forecasting Models via Stochastic Contraction and ABSM Scaling

This repository contains the implementation, experiments, and supplementary materials for the paper “Adaptive Post-Prediction Stabilization for Forecasting Models via Stochastic Contraction and ABSM Scaling.” The work introduces a model-agnostic post-prediction stabilization framework that operates on forecasting outputs rather than modifying the underlying predictive model. The proposed method combines stochastic contraction theory with an adaptive balancing scaling mechanism (ABSM) to enforce bounded and stable trajectory dynamics under noise. Unlike conventional approaches that optimize predictive accuracy, this framework focuses on stabilizing the geometry of prediction trajectories in time series forecasting systems. The method is evaluated across statistical, machine learning, and deep learning forecasting models including ARIMA, LSTM, GRU, Random Forest, and XGBoost. The repository includes: Implementation of ABSM-based stabilization operator Stochastic contraction simulation framework Experimental evaluation scripts Visualization tools for trajectory stability and variance analysis Reproducible Colab notebook All experiments are fully reproducible and designed to support further research in stable forecasting systems, dynamical systems theory in machine learning, and post-processing correction operators.