用未来预测反推现在决策，提升气象实时预报与干预精度
Back To The Future: A Hybrid Transformer-XGBoost Model for Action-oriented Future-proofing Nowcasting
- 融合Transformer预测未来+XGBoost优化当前决策的双阶段框架
- 在气象数据上实现更精准的短期预报，并支持可操作干预
- 适合需要实时响应与因果决策的场景，如灾害预警与资源调度
受经典电影《回到未来》启发，本文提出一种创新的自适应实时预报方法，重新构想当前行动与未来结果之间的关系。类比电影中穿越时间改变过去以改善未来，本框架利用对未来状态的预测来调整当前条件。该混合模型结合了Transformer的未来预测能力（未来预判者）与XGBoost的可解释性及高效决策能力（决策执行者），形成未来预测与当前调控的闭环。基于气象数据集的实验表明，该框架不仅显著提升了预报准确性，还能为实时应用提供可执行的干预策略。
原文摘要 · Abstract (English)
Inspired by the iconic movie Back to the Future, this paper explores an innovative adaptive nowcasting approach that reimagines the relationship between present actions and future outcomes. In the movie, characters travel through time to manipulate past events, aiming to create a better future. Analogously, our framework employs predictive insights about the future to inform and adjust present conditions. This dual-stage model integrates the forecasting power of Transformers (future visionary) with the interpretability and efficiency of XGBoost (decision maker), enabling a seamless loop of future prediction and present adaptation. Through experimentation with meteorological datasets, we demonstrate the framework's advantage in achieving more accurate forecasting while guiding actionable interventions for real-time applications.
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