Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control

Mohammad Al Ridhawi, Mahtab Haj Ali, Hussein Al Osman|March 19, 2026arXiv

Key Takeaway

Treating all market conditions the same hurts prediction accuracy; this framework learns to detect regime shifts automatically and uses specialized models for each, improving performance especially during volatile periods without requiring manual market labeling.

Summary

This paper presents an adaptive stock price prediction system that automatically detects market regime changes (stable vs. volatile periods) and routes data through specialized prediction models.

architecture applications

Key Terms

autoencoder transformer-architecture soft-actor-critic regime-detection reconstruction-error