Sousa, JoãoHenriques, Roberto2026-06-162026-06-162026-060941-0643PURE: 164809148PURE UUID: ed144499-9f15-4bfc-aabd-e56c163a97c0Scopus: 105041916833ORCID: /0000-0002-4862-8177/work/217889930http://hdl.handle.net/10362/203794Sousa, J., & Henriques, R. (2026). T2f: Actor-critic reinforcement learning for time-series forecasting. Neural Computing and Applications, 38(12), Article 510. https://doi.org/10.1007/s00521-026-12209-6Time-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.352762781engTime-series forecastingActor-critic methodsTransformer architectureReinforcement learningEnsemble learningSoftwareArtificial Intelligencejournal article10.1007/s00521-026-12209-6https://www.scopus.com/pages/publications/105041916833https://github.com/jfpsousa/t2fhttps://doi.org/10.5281/zenodo.20718387