Clustering and Forecasting Multiple Functional Time Series
多重函数型时间序列的聚类与预测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：摘要明确结合多国函数型时间序列、固定效应面板建模和死亡率预测，支持这三个方向。
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…
关注这篇论文的作者
多重函数型时间序列的聚类与预测
- 研究问题
- 多国年龄别死亡率按地理等标签分组后仍可能异质，如何同时利用曲线内部变化和时间动态构造更同质的预测分组？
- 方法与思路
- 作者采用带固定效应的函数型面板模型提取共同特征，将其分为函数时间趋势与曲线变化模式，并据此聚类和预测。
- 材料陈述的贡献
- 摘要称聚类在模拟中优于所比较方法且能处理特征值缓慢衰减的情形，多国死亡率应用也显示较准确的预测。
- 阅读关注点
- 应核对共同特征提取、距离与聚类数选择，以及聚类是否只使用训练期数据、预测基准和慢衰减情形的具体设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未说明完整理论条件、样本范围或误差数值，报告的优势依赖比较设置。
原文摘要
Modelling and forecasting homogeneous age-specific mortality rates of multiple countries could lead to improvements in long-term forecasting. Data fed into joint models are often grouped according to nominal attributes, such as geographic regions, ethnic groups, and socioeconomic status, which may still contain heterogeneity and deteriorate the forecast results. Our paper proposes a novel clustering technique to pursue homogeneity among multiple functional time series based on functional panel data modelling to address this issue. Using a functional panel data model with fixed effects, we can extract common functional time series features. These common features could be decomposed into two components: the functional time trend and the mode of variations of functions (functional pattern). The functional time trend reflects the dynamics across time, while the functional pattern captures the fluctuations within curves. The proposed clustering method searches for homogeneous age-specific mortality rates of multiple countries by accounting for both the modes of variations and the temporal dynamics among curves. We demonstrate that the proposed clustering technique outperforms other existing methods through a Monte Carlo simulation and could handle complicated cases with slow decaying eigenvalues. In empirical data analysis, we find that the clustering results of age-specific mortality rates can be explained by the combination of geographic region, ethnic groups, and socioeconomic status. We further show that our model produces more accurate forecasts than several benchmark methods in forecasting age-specific mortality rates.