Quantile-based fuzzy clustering of multivariate time series in the frequency domain
频域中基于分位数的多元时间序列模糊聚类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：摘要核心是多元时间序列依赖结构的频域特征与聚类，金融数据应用未明确资产定价、波动率或其他金融细分目标。
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…
频域中基于分位数的多元时间序列模糊聚类
- 研究问题
- 当多元时间序列的依赖结构存在差异、重叠或动态变化时，如何以不同隶属程度进行聚类，而非强制每条序列只属于一类？
- 方法与思路
- 作者先估计分位数交叉谱密度特征，再用主成分降维，最后依据保留主成分的平方欧氏距离实施模糊 C 均值或 C 中心点聚类。
- 材料陈述的贡献
- 摘要称，方法在多种线性、非线性和动态相关模拟中优于所比较方案，并从分组质量与重叠识别两方面评价，另有空气和金融应用。
- 阅读关注点
- 应核对分位水平与频率选择、交叉谱估计误差、主成分保留规则，以及聚类数、模糊程度和比较方案的调参是否公平。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要所称全面优胜限于其模拟设计，未说明完整参数设置或应用评价结果。
原文摘要
A novel procedure to perform fuzzy clustering of multivariate time series generated from different dependence models is proposed. Different amounts of dissimilarity between the generating models or changes on the dynamic behaviours over time are some arguments justifying a fuzzy approach, where each series is associated to all the clusters with specific membership levels. Our procedure considers quantile-based cross-spectral features and consists of three stages: (i) each element is characterized by a vector of proper estimates of the quantile cross-spectral densities, (ii) principal component analysis is carried out to capture the main differences reducing the effects of the noise, and (iii) the squared Euclidean distance between the first retained principal components is used to perform clustering through the standard fuzzy C-means and fuzzy C-medoids algorithms. The performance of the proposed approach is evaluated in a broad simulation study where several types of generating processes are considered, including linear, nonlinear and dynamic conditional correlation models. Assessment is done in two different ways: by directly measuring the quality of the resulting fuzzy partition and by taking into account the ability of the technique to determine the overlapping nature of series located equidistant from well-defined clusters. The procedure is compared with the few alternatives suggested in the literature, substantially outperforming all of them whatever the underlying process and the evaluation scheme. Two specific applications involving air quality and financial databases illustrate the usefulness of our approach.