Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data
超越点预测：时间序列与时空数据概率预测综述AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确综述时间序列与时空数据的概率预测，并比较预测表现和不确定性校准。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
超越点预测：时间序列与时空数据概率预测综述
- 研究问题
- 如何统一理解时间序列与时空概率预测中的不同不确定性建模方式，并在校准、精度、分布集中程度和计算成本之间选择？
- 方法与思路
- 作者按照不确定性进入预测流程的位置与方式组织方法分类，覆盖校准、贝叶斯及生成模型，并开展跨范式预测比较。
- 材料陈述的贡献
- 摘要提出统一分类与方法选择框架；实证比较称不存在全面占优的范式，并归纳依赖结构变化、极端事件等未解决问题。
- 阅读关注点
- 应核对分类边界、比较任务与训练预算是否一致，以及校准、分布集中程度和预测精度的指标如何影响方法选择结论。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未列明数据集、模型配置及比较数值，不能据此认定某方法普遍占优。
原文摘要
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.