用大模型当智能裁判，协调多个预测模型提升准确性。
Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting
- 让大模型扮演裁判角色，评估并解释多个模型的权重分配。
- 在97个场景中，预测误差比现有方法降低12.3%（CRPS）。
- 适合需要可解释性预测的金融、气象等领域应用。
时间序列基础模型虽多，但无一始终领先，关键挑战在于如何构建兼具性能与可解释性的集成模型。尽管大语言模型具备强大推理能力，但直接用于时间序列预测效果不佳。本文将大语言模型重定位为智能裁判，通过基于SHAP的忠实度评分进行R1风格微调，使其能将集成权重解读为对时间动态的因果解释。该模型通过多轮对话，实现前瞻性评估、因果解释和策略自适应优化。在涵盖23个数据集的97个设置下，于GIFT-Eval基准测试中，本方法在CRPS和MASE指标上显著优于主流时间序列基础模型，刷新了当前最优结果。
原文摘要 · Abstract (English)
The proliferation of time series foundation models has created a landscape where no single method achieves consistent superiority, framing the central challenge not as finding the best model, but as orchestrating an optimal ensemble with interpretability. While Large Language Models (LLMs) offer powerful reasoning capabilities, their direct application to time series forecasting has proven ineffective. We address this gap by repositioning the LLM as an intelligent judge that evaluates, explains, and strategically coordinates an ensemble of foundation models. To overcome the LLM's inherent lack of domain-specific knowledge on time series, we introduce an R1-style finetuning process, guided by SHAP-based faithfulness scores, which teaches the model to interpret ensemble weights as meaningful causal statements about temporal dynamics. The trained agent then engages in iterative, multi-turn conversations to perform forward-looking assessments, provide causally-grounded explanations for its weighting decisions, and adaptively refine the optimization strategy. Validated on the GIFT-Eval benchmark on 23 datasets across 97 settings, our approach significantly outperforms leading time series foundation models on both CRPS and MASE metrics, establishing new state-of-the-art results.
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