AI RESEARCH
DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
arXiv CS.LG
•
ArXi:2605.14632v1 Announce Type: new Forecasting multivariate hidden Marko processes is challenging due to nonlinear and nonstationary observations, latent state transitions, and cross-sequence dependencies. While deep learning methods achieve strong predictive accuracy, they typically lack explicit state modeling, whereas Hidden Marko Models (HMMs) provide interpretable latent states but struggle with complex nonlinear emissions and scalability.