Time series forecasting plays a crucial role in contemporary engineering information systems for supporting decision-making across various industries, where Recurrent Neural Networks (RNNs) have been widely adopted due to their capability in modeling sequential data. Conventional RNN-based predictors adopt an encoder-only strategy with sliding historical windows as inputs to forecast future values. However, this approach treats all time steps and hidden states equally without considering their distinct contributions to forecasting, leading to suboptimal performance. To address this limitation, we propose a novel Reinforced Recurrent Encoder with Prediction-oriented Proximal Policy Optimization, RRE-PPO4Pred, which significantly improves time series modeling capacity and forecasting accuracy of the RNN models. The core innovations of this method are: (1) A novel Reinforced Recurrent Encoder (RRE) framework that enhances RNNs by formulating their internal adaptation as a Markov Decision Process, creating a unified decision environment capable of learning input feature selection, hidden skip connection, and output target selection; (2) An improved Prediction-oriented Proximal Policy Optimization algorithm, termed PPO4Pred, which is equipped with a Transformer-based agent for temporal reasoning and develops a dynamic transition sampling strategy to enhance sampling efficiency; (3) A co-evolutionary optimization paradigm to facilitate the learning of the RNN predictor and the policy agent, providing adaptive and interactive time series modeling. Comprehensive evaluations on five real-world datasets indicate that our method consistently outperforms existing baselines, and attains accuracy better than state-of-the-art Transformer models, thus providing an advanced time series predictor in engineering informatics.

翻译：时间序列预测在当代工程信息系统中对支持各行业决策制定起着至关重要的作用，其中循环神经网络因其在序列数据建模方面的能力而被广泛采用。传统的基于RNN的预测器采用仅编码器策略，以滑动历史窗口作为输入来预测未来值。然而，这种方法平等对待所有时间步和隐藏状态，未考虑它们对预测的不同贡献，导致性能欠佳。为克服这一局限，我们提出了一种新颖的基于预测导向近端策略优化的强化循环编码器，RRE-PPO4Pred，它显著提升了RNN模型的时间序列建模能力和预测精度。该方法的核心创新在于：（1）一种新颖的强化循环编码器框架，通过将其内部适应过程建模为马尔可夫决策过程来增强RNN，创建了一个能够学习输入特征选择、隐藏层跳跃连接和输出目标选择的统一决策环境；（2）一种改进的预测导向近端策略优化算法，称为PPO4Pred，该算法配备了一个基于Transformer的智能体进行时序推理，并开发了动态转移采样策略以提高采样效率；（3）一种协同进化优化范式，以促进RNN预测器和策略智能体的学习，提供自适应且交互式的时间序列建模。在五个真实世界数据集上的综合评估表明，我们的方法始终优于现有基线，并且达到了比最先进的Transformer模型更优的精度，从而为工程信息学提供了一种先进的时间序列预测器。