用图自编码器生成高斯混合嵌入，提升时间序列聚类效果
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework
- 基于图结构的变分混合自编码器，捕捉时间序列深层依赖
- 在真实金融数据上显著优于现有聚类方法，提升分离度
- 适合金融分析、市场预测等需要发现复杂关系的场景
时间序列数据分析广泛应用于金融、医疗和环境监测等领域。传统聚类方法难以捕捉数据中的复杂时序依赖关系。本文提出变分混合图自编码器（VMGAE），一种基于图结构的时间序列聚类方法，利用图的结构性优势捕捉更丰富的数据关联，并生成高斯混合嵌入以增强可分性。实验对比表明，该方法显著优于当前主流的时间序列聚类技术。进一步在真实金融数据上验证，揭示股票市场的社区结构，为市场预测、投资组合优化和风险管理提供更深入洞察。
原文摘要 · Abstract (English)
Time series data analysis is prevalent across various domains, including finance, healthcare, and environmental monitoring. Traditional time series clustering methods often struggle to capture the complex temporal dependencies inherent in such data. In this paper, we propose the Variational Mixture Graph Autoencoder (VMGAE), a graph-based approach for time series clustering that leverages the structural advantages of graphs to capture enriched data relationships and produces Gaussian mixture embeddings for improved separability. Comparisons with baseline methods are included with experimental results, demonstrating that our method significantly outperforms state-of-the-art time-series clustering techniques. We further validate our method on real-world financial data, highlighting its practical applications in finance. By uncovering community structures in stock markets, our method provides deeper insights into stock relationships, benefiting market prediction, portfolio optimization, and risk management.
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