发表机构
Macquarie University（麦考瑞大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对密度值时间序列的非负和积分为一约束，提出基于中心化对数比率和累积分布函数的两种变换，结合多人口建模与差距建模，在日本死亡数据上评估预测精度并给出建议。
AI 中文摘要
密度值时间序列在实践中很常见。然而，由于这些函数是非负的且必须积分为一，它们不构成线性向量空间，这使得标准时间序列方法的直接应用变得复杂。为解决这些约束，我们引入了两种基于中心化对数比率变换和累积分布函数的变换，用于建模和预测由多个密度表示的平衡面板数据。两种变换都将密度双射地映射到一个无约束的函数空间，在该空间中可以直接建模横截面和序列依赖性。在所得的无约束空间内，我们考虑了两种方法：多人口函数型时间序列建模以考虑人口间的相关性，以及性别和地区之间的差距建模。利用1973年至2024年日本次国家级年龄别生命表死亡计数，我们评估并比较了每种变换下两种方法的一步至二十步超前点预测和区间预测的准确性，并提供了一些一般性建议。
英文摘要
Density-valued time series are common in practice. However, since these functions are non-negative and must integrate to one, they do not form a linear vector space, which complicates the direct application of standard time-series methods. To address these constraints, we introduce two transformations, based on the centered log-ratio transformation and the cumulative distribution function, for modeling and forecasting balanced panel data represented by multiple densities. Both transformations map densities bijectively into an unconstrained function space in which cross-sectional and serial dependence can be modeled directly. Within the resulting unconstrained space, we consider two approaches: multi-population functional time-series modeling to account for correlations among populations, and gap modeling between gender and region. Using Japanese subnational age-specific life-table death counts from 1973 to 2024, we evaluate and compare the one- to 20-step-ahead point and interval forecast accuracy of the two approaches for each transformation, and offer some general recommendations.
Comments42 pages,12 figures, 10 tables
