Rethinking Recurrent Neural Networks for Time Series Forecasting: A Reinforced Recurrent Encoder with Prediction-Oriented Proximal Policy Optimization

2026-01-07

arXiv.org

AI Summary

This work addresses the limitation of traditional recurrent neural networks (RNNs) in time series forecasting, which treat all time steps and hidden states uniformly despite their varying contributions to prediction accuracy. To overcome this, the authors propose the Reinforced Recurrent Encoder (RRE) framework, which models the internal RNN dynamics as a Markov decision process. Within this framework, they introduce PPO4Pred—a prediction-oriented proximal policy optimization algorithm that integrates a Transformer architecture with a dynamic transition sampling strategy—to jointly optimize feature selection, hidden skip connections, and output prediction. Furthermore, a co-evolutionary mechanism is incorporated to enhance model adaptability. Evaluated on five real-world datasets, the proposed method consistently outperforms existing baselines and achieves superior predictive accuracy compared to state-of-the-art Transformer-based models.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Time Series Forecasting
• Recurrent Neural Networks
• Encoder-only Strategy
• Hidden State Contribution
• Forecasting Performance

Innovation

• Reinforced Recurrent Encoder
• Prediction-oriented PPO
• Markov Decision Process
• Co-evolutionary Optimization
• Time Series Forecasting