用强化学习联合预测多变量隐状态与观测值，兼顾准确与可解释性。
DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
- 结合深度网络与强化学习，显式建模复杂非线性观测与离散隐状态
- 在多个数据集上优于传统HMM、纯深度模型及混合方法，且状态估计可靠
- 适合需要理解动态演化过程的场景，如金融、医疗时间序列分析
多变量隐马尔可夫过程的预测因非线性、非平稳观测、潜在状态转移及序列间依赖而困难。深度学习虽具高预测精度，但缺乏显式状态建模；而隐马尔可夫模型（HMM）虽可解释，却难以处理复杂非线性观测且扩展性差。为此，本文提出DRL-STAF——一种基于深度强化学习的态感知预测框架，可联合预测下一步观测并估计对应隐状态。该框架利用深度神经网络建模复杂非线性观测，通过强化学习估计离散隐状态，降低对预设转移结构的依赖，灵活适应多样时序动态。特别地，DRL-STAF缓解了传统多变量HMM方法面临的状态空间爆炸问题。大量实验表明，DRL-STAF在多数情况下优于HMM变体、独立深度学习模型及现有深度学习-HMM混合方法，同时提供可靠的隐状态估计。
原文摘要 · Abstract (English)
Forecasting multivariate hidden Markov 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 Markov Models (HMMs) provide interpretable latent states but struggle with complex nonlinear emissions and scalability. To address these limitations, we propose DRL-STAF, a Deep Reinforcement Learning based STate-Aware Forecasting framework that jointly predicts next-step observations and estimates the corresponding hidden states for complex multivariate hidden Markov processes. Specifically, DRL-STAF models complex nonlinear emissions using deep neural networks and estimates discrete hidden states using reinforcement learning, reducing the reliance on predefined transition structures and enabling flexible adaptation to diverse temporal dynamics. In particular, DRL-STAF mitigates the state-space explosion encountered by typical multivariate HMM-based methods. Extensive experiments demonstrate that DRL-STAF outperforms HMM variants, standalone deep learning models, and existing DL-HMM hybrids in most cases, while also providing reliable hidden-state estimates.
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