零样本预测混沌系统，大模型表现媲美定制模型
Zero-shot forecasting of chaotic systems
- 用上下文学习实现混沌系统零样本预测
- 10^8个时间点上性能接近专用模型
- 适合研究复杂非线性系统的学者使用
时间序列预测传统上需要为特定任务定制训练模型。近期，受大型语言模型成功启发，基于海量跨领域时间序列数据预训练的基础模型成为通用时间序列预测的有力候选。这类模型的核心能力是零样本学习：仅凭少量上下文数据即可预测新系统，无需重新训练或微调。本文评估该范式在混沌系统预测中的适用性。在135种不同混沌动力系统、共10^8个时间点的测试中，基础模型的预测性能与定制模型（如NBEATS、TiDE等）相当，尤其在训练数据有限时表现更优。有趣的是，即使点预测失败，大模型仍能保持混沌吸引子的几何与统计特性。我们归因于模型具备上下文学习能力，并识别出‘上下文复述’是其捕捉混沌系统长期行为的简单机制。结果表明，基础模型有望成为探究非线性复杂系统的重要工具。
原文摘要 · Abstract (English)
Time-series forecasting is a challenging problem that traditionally requires specialized models custom-trained for the specific task at hand. Recently, inspired by the success of large language models, foundation models pre-trained on vast amounts of time-series data from diverse domains have emerged as a promising candidate for general-purpose time-series forecasting. The defining characteristic of these foundation models is their ability to perform zero-shot learning, that is, forecasting a new system from limited context data without explicit re-training or fine-tuning. Here, we evaluate whether the zero-shot learning paradigm extends to the challenging task of forecasting chaotic systems. Across 135 distinct chaotic dynamical systems and $10^8$ timepoints, we find that foundation models produce competitive forecasts compared to custom-trained models (including NBEATS, TiDE, etc.), particularly when training data is limited. Interestingly, even after point forecasts fail, large foundation models are able to preserve the geometric and statistical properties of the chaotic attractors. We attribute this success to foundation models' ability to perform in-context learning and identify context parroting as a simple mechanism used by these models to capture the long-term behavior of chaotic dynamical systems. Our results highlight the potential of foundation models as a tool for probing nonlinear and complex systems.
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