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

Source: arXiv - 2601.03683v1

Overview

The paper introduces RRE‑PPO4Pred, a new way to boost recurrent neural networks (RNNs) for time‑series forecasting. By treating the hidden‑state dynamics of an RNN as a decision‑making problem and training a reinforcement‑learning (RL) agent with a prediction‑focused variant of Proximal Policy Optimization (PPO), the authors achieve consistently higher accuracy than both classic RNN baselines and recent Transformer‑based forecasters on several real‑world datasets.

Key Contributions

• Reinforced Recurrent Encoder (RRE) – casts the internal operations of an RNN (feature selection, hidden‑state skipping, and output target choice) as a Markov Decision Process, enabling the model to learn where and when to focus its attention.
• Prediction‑oriented PPO (PPO4Pred) – a customized PPO algorithm that uses a lightweight Transformer as the policy network, adds a loss term that directly rewards forecasting quality, and employs a dynamic transition‑sampling scheme to reduce variance in gradient estimates.
• Co‑evolutionary training loop – simultaneously optimizes the base RNN predictor and the RL policy, allowing them to adapt to each other’s improvements throughout training.
• Empirical superiority – extensive experiments on five diverse, industry‑relevant time‑series benchmarks show RRE‑PPO4Pred beating strong RNN baselines, classical statistical models, and even state‑of‑the‑art Transformer forecasters.

Methodology

1. Problem framing – The forecasting task is split into two interacting components:

   • An RNN encoder‑decoder that still processes the raw sequence but now receives policy‑guided inputs (e.g., which past timestamps to attend to).
   • A policy agent (a small Transformer) that observes the current hidden state and decides three actions:
     1. Input feature selection – pick a subset of the sliding window to feed forward.
     2. Hidden‑state skip connection – optionally bypass certain recurrent updates to avoid over‑fitting noisy steps.
     3. Target selection – choose which future horizon(s) to predict at the current step.

2. Markov Decision Process (MDP) – Each time step constitutes a state; the agent’s actions transition the RNN to the next state. The reward is defined as the negative forecasting loss (e.g., MAE) after the RNN produces its prediction, encouraging actions that directly improve accuracy.

3. PPO4Pred – The classic PPO objective is augmented with a prediction‑oriented term that penalizes large forecast errors, and the clipping mechanism is tuned for the high‑dimensional action space. The Transformer policy is trained with mini‑batches of dynamically sampled transitions, which concentrates learning on informative states (e.g., periods with high volatility).

4. Co‑evolutionary loop – Training alternates between:

   • Updating the RNN parameters using standard back‑propagation on the forecast loss (conditioned on the current policy).
   • Updating the policy network via PPO4Pred using the latest RNN predictions as part of the environment feedback.

   This back‑and‑forth continues until convergence, yielding a tightly coupled predictor–policy pair.

Results & Findings

• Consistent gains of 5–12 % over the strongest Transformer baselines.
• Ablation studies show that removing the policy‑guided input selection or the skip‑connection action drops performance by ~4 %, confirming each component’s contribution.
• Training efficiency: thanks to the dynamic transition sampler, PPO4Pred converges ~30 % faster than vanilla PPO on the same hardware.

Practical Implications

• Better resource utilization – By learning to skip irrelevant hidden updates, the model reduces unnecessary computations, which can translate into lower inference latency on edge devices (e.g., IoT gateways monitoring sensor streams).
• Adaptive forecasting pipelines – The policy can be retrained on new data without redesigning the entire RNN architecture, making it easier to integrate into existing time‑series platforms that already rely on LSTM/GRU models.
• Explainability hooks – The actions (which timestamps were selected, which skips were taken) provide a transparent view of why the model focused on certain periods, aiding debugging and compliance in regulated sectors like energy or finance.
• Plug‑and‑play upgrade – Since the RRE sits on top of any standard recurrent cell, teams can upgrade legacy forecasting services by swapping in the RRE‑PPO4Pred wrapper rather than rebuilding from scratch.

Limitations & Future Work

• Complexity of training – The co‑evolutionary loop adds extra hyper‑parameters (e.g., PPO clipping, transition‑sampling schedule) that require careful tuning, potentially raising the barrier for small teams.
• Scalability to ultra‑long horizons – While the method excels on horizons up to a few hundred steps, the action space grows with window size, and the authors note diminishing returns beyond that point.
• Domain‑specific reward shaping – The current reward is a generic negative loss; tailoring it to business metrics (e.g., cost of under‑forecasting) could further improve real‑world impact.
• Future directions suggested include:
  1. Hierarchical policies that operate at multiple temporal resolutions.
  2. Incorporating external covariates (weather, events) into the decision process.
  3. Extending the framework to multimodal time‑series (e.g., video + sensor streams).

Authors

• Xin Lai
• Shiming Deng
• Lu Yu
• Yumin Lai
• Shenghao Qiao
• Xinze Zhang

Paper Information

• arXiv ID: 2601.03683v1
• Categories: cs.LG, cs.NE
• Published: January 7, 2026
• PDF: Download PDF