发表机构
Sorbonne University Abu Dhabi; Sorbonne Université（阿布扎比索邦大学; 索邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述统一了概率预测方法论，通过跨范式实证研究揭示不同不确定性量化范式的适用性，并指出未来挑战。
AI 中文摘要
概率预测在不确定性下的决策中至关重要，然而其方法论领域在时间序列和时空预测、统计建模、机器学习以及深度生成建模方面已变得日益碎片化。本综述通过组织概率预测方法，根据不确定性在预测流程中被引入的位置和方式，提出了一个统一的视角。我们的分类法将模型无关的方法（包括集成方法和无分布校准）与模型内在的方法（涵盖贝叶斯建模、参数化预测分布、分布回归和现代生成模型）联系起来，并进一步考察了时间序列基础模型的新兴作用。除了方法论分类，我们还识别了不同范式所代表的假设、计算需求和不确定性形式，并将这些区别转化为数据驱动和领域特定的方法选择指导。本综述辅以一项跨范式的实证研究，涵盖单变量、多变量和时空预测任务。结果表明，没有任何单一的不确定性量化范式在所有情境中占主导地位。校准、锐度、预测准确性和计算效率可能导致截然不同的模型偏好，而表达性生成模型和零样本基础模型则展现出显著不同的精度-效率权衡。最后，我们指出了围绕演化依赖结构中的不确定性、物理信息预测分布、极端事件预测、计数型、方向性和连续时间序列处理以及统一软件资源开发等方面的未解挑战。本综述为概率预测研究提供了概念框架和实践路线图。
英文摘要
Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across temporal and spatiotemporal forecasting, statistical modeling, machine learning, and deep generative modeling. This survey develops a unified perspective by organizing probabilistic forecasting methods according to where and how uncertainty is introduced into the forecasting pipeline. Our taxonomy connects model-agnostic approaches including ensembles and distribution-free calibration, with model-intrinsic approaches spanning Bayesian modeling, parametric predictive distributions, distributional regression, and modern generative models, and further examines the emerging role of time series foundation models. Beyond methodological synthesis, we identify the assumptions, computational demands, and forms of uncertainty represented by different paradigms, and translate these distinctions into data-driven and domain-specific guidance for method selection. We complement the survey with a cross-paradigm empirical study on univariate, multivariate, and spatiotemporal forecasting tasks. The results reveal that no single uncertainty-quantification paradigm dominates across settings. Calibration, sharpness, predictive accuracy, and computational efficiency can lead to substantially different model preferences, while expressive generative models and zero-shot foundation models exhibit markedly different accuracy-efficiency trade-offs. Lastly, we identify unresolved challenges surrounding uncertainty in evolving dependency structures, physics-informed predictive distributions, forecasting extreme events, handling count-valued, directional, and continuous-time series, and the development of unified software resources. Our survey provides both a conceptual framework and a practical roadmap for probabilistic forecasting research.
