针对洛伦兹挑战赛，设计了按指标匹配的混合预测系统。
Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge
- 根据不同评估指标，分别选用去噪器、微分方程拟合和分布替换策略。
- 在公开榜单上达到83.83551分，后续优化至83.85529分。
- 适合对多目标时间序列预测感兴趣的科研人员参考。
我们介绍了应对CTF4Science洛伦兹挑战赛的方法，该基准包含短时预测、长期分布匹配与九组任务的轨迹重构。关键发现是单一模型无法在所有指标上领先。因此，我们构建了一个指标感知的混合系统：(1) 使用合成预训练去噪器进行完整轨迹重建；(2) 采用洛伦兹微分方程拟合并结合轨迹投射法预测前20步；(3) 利用合成洛伦兹数据集进行尾部分布替换以应对长期评估。该系统的一个成熟版本在公开排行榜上获得83.83551分，后续小规模堆叠同样思路的方案提升至83.85529分。本文聚焦于更简洁的中间系统，因其完整体现方法且易于复现分析，最终提交可视为对同一核心架构的保守扩展。
原文摘要 · Abstract (English)
We describe our approach to the CTF4Science Lorenz challenge, a benchmark that mixes short-horizon forecasting, long-time distribution matching, and trajectory reconstruction across nine task pairs. The key discovery is that no single model family dominated all metrics. Instead, we built a metric-aware hybrid system that assigned a different predictor to each metric family: (1) synthetic-pretrained denoisers for full-trajectory reconstruction, (2) Lorenz ODE fitting and trajectory shooting for the first 20 forecast steps, and (3) histogram-tail substitution using synthetic Lorenz libraries for long-time evaluation. A representative mature submission from this system family scored 83.83551 on the public leaderboard, and a small follow-up stack of the same ideas reached 83.85529. We focus on the cleaner intermediate system because it captures the full method while remaining simple enough to reproduce and analyze, while the final submission can be understood as a conservative extension of the same backbone.
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