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<p>Time-series forecasting of multiple related sequences presents unique challenges due to the complex interplay between individual series characteristics and global patterns. We present <i>T2f</i>, a forecasting method combining ensemble learning with an actor-critic architecture based on the Twin Delayed Deep Deterministic algorithm (TD3). <i>T2f</i> balances local and global patterns through both its architecture and learning approaches, integrating transformer-based pattern recognition with reinforcement learning for dynamic model selection. Our method incorporates temporal attention mechanisms and context-aware error measurement, aligning forecasting objectives with practical decision-making priorities. Comprehensive ablation studies demonstrate that <i>T2f</i>’s components provide synergistic benefits: the TD3-based optimizer contributes 18.8% error reduction over static weighting, while temporal attention adds 8.0% improvement, with the integrated system outperforming simple ensemble baselines by over 20%. Experimental results across five diverse datasets indicate <i>T2f</i> reduced mean absolute error by over 30% compared to statistical models and achieved up to 40% better performance on context-weighted metrics than competing approaches. While specialized models occasionally outperformed <i>T2f</i> on highly regular patterns, it consistently showed superior adaptability to contextual weights with faster convergence, typically reaching near-optimal performance within 25 epochs compared to 40+ for alternative methods, particularly on datasets with complex temporal dynamics.</p>

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T2f: Actor-critic reinforcement learning for time-series forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • João Sousa,
  • Roberto Henriques

摘要

Time-series forecasting of multiple related sequences presents unique challenges due to the complex interplay between individual series characteristics and global patterns. We present T2f, a forecasting method combining ensemble learning with an actor-critic architecture based on the Twin Delayed Deep Deterministic algorithm (TD3). T2f balances local and global patterns through both its architecture and learning approaches, integrating transformer-based pattern recognition with reinforcement learning for dynamic model selection. Our method incorporates temporal attention mechanisms and context-aware error measurement, aligning forecasting objectives with practical decision-making priorities. Comprehensive ablation studies demonstrate that T2f’s components provide synergistic benefits: the TD3-based optimizer contributes 18.8% error reduction over static weighting, while temporal attention adds 8.0% improvement, with the integrated system outperforming simple ensemble baselines by over 20%. Experimental results across five diverse datasets indicate T2f reduced mean absolute error by over 30% compared to statistical models and achieved up to 40% better performance on context-weighted metrics than competing approaches. While specialized models occasionally outperformed T2f on highly regular patterns, it consistently showed superior adaptability to contextual weights with faster convergence, typically reaching near-optimal performance within 25 epochs compared to 40+ for alternative methods, particularly on datasets with complex temporal dynamics.