AI 中文总结
该综述围绕时间序列预测展开，聚焦基于人工智能的三大进展，通过给定过去情况下未来的条件分布与向量自回归传统相联系，围绕高维度、非平稳性和非线性挑战展开，指出现代方法虽有进展但缺相关工具，还概述了计量经济学工具重要的开放问题。
AI 中文摘要
预测是时间序列分析的核心目标。本综述聚焦近期基于人工智能的时间序列预测的三大主要进展：变压器、用于零样本预测的大型预训练模型以及基于扩散的生成式预测器。我们通过一个共同对象——给定过去情况下未来的条件分布，将这些方法与围绕向量自回归（VAR）构建的计量经济学传统联系起来。综述围绕三个长期存在的挑战展开：高维度、非平稳性和非线性。我们认为现代方法通过扩展经典预测模板取得了进展：它们允许更灵活的动态变化，使用更大的信息集和训练语料库，并表示更丰富的预测分布。然而，它们往往缺乏使经典模型对测试、解释和政策分析有用的推断和结构工具。最后，我们概述了计量经济学工具仍然重要的开放问题。
英文摘要
Forecasting is a central goal of time-series analysis. This review centers on three major developments in recent AI-based time-series forecasting: transformers, large pretrained models for zero-shot forecasting, and diffusion-based generative forecasters. We connect these methods to the econometric tradition built around the vector autoregression (VAR) through a common object: the conditional distribution of the future given the past. The review is organized around three long-standing challenges: \emph{high dimensionality}, \emph{nonstationarity}, and \emph{nonlinearity}. We argue that modern methods make progress by expanding the classical forecasting template: they allow more flexible dynamics, use larger information sets and training corpora, and represent richer predictive distributions. Yet they often lack the inferential and structural tools that make classical models useful for testing, explanation, and policy analysis. We close by outlining open problems where econometric tools remain important.
