标题： 真正的零样本推理在保局长期统计的动力系统中的应用 显示英文标题

标题： True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics

摘要： 复杂且随时间演化的现象，从气候到大脑活动，都由动力系统（DS）所支配。 DS重构（DSR）旨在从观测数据中推断这些系统的生成代理模型，重现它们的长期行为。 现有的DSR方法需要针对任何新观察到的系统进行专门训练，缺乏像大型语言模型（LLMs）那样的零样本和上下文推理能力。 在这里我们介绍了DynaMix，这是一种新颖的多变量基于ALRNN的专家混合架构，预先训练用于DSR，这是首个能够零样本泛化到非领域内DS的DSR模型。 仅从提供的上下文信号出发，无需任何再训练，DynaMix就能忠实地预测新型DS的长期演化，而现有的时间序列（TS）基础模型，比如Chronos，在这方面会失败——而且DynaMix的参数量仅为前者的几分之一，推理速度则快几个数量级。 DynaMix在长时统计方面优于TS基础模型，并且通常在短期预测上也表现更好，即使是在用于训练和评估TS模型的真实世界时间序列，如交通或天气数据上也是如此，但这些数据完全不在DynaMix的训练语料库中。 我们展示了TS模型在DSR问题上的某些失效模式，并得出结论认为基于DS原理构建的模型也可能在推进TS预测领域方面具有巨大潜力。 显示英文摘要

摘要： Complex, temporally evolving phenomena, from climate to brain activity, are governed by dynamical systems (DS). DS reconstruction (DSR) seeks to infer generative surrogate models of these from observed data, reproducing their long-term behavior. Existing DSR approaches require purpose-training for any new system observed, lacking the zero-shot and in-context inference capabilities known from LLMs. Here we introduce DynaMix, a novel multivariate ALRNN-based mixture-of-experts architecture pre-trained for DSR, the first DSR model able to generalize zero-shot to out-of-domain DS. Just from a provided context signal, without any re-training, DynaMix faithfully forecasts the long-term evolution of novel DS where existing time series (TS) foundation models, like Chronos, fail -- at a fraction of the number of parameters and orders of magnitude faster inference times. DynaMix outperforms TS foundation models in terms of long-term statistics, and often also short-term forecasts, even on real-world time series, like traffic or weather data, typically used for training and evaluating TS models, but not at all part of DynaMix' training corpus. We illustrate some of the failure modes of TS models for DSR problems, and conclude that models built on DS principles may bear a huge potential also for advancing the TS prediction field.