Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework

Autor:	Afzali, Amirabbas, Hosseini, Hesam, Mirzai, Mohmmadamin, Amini, Arash
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: First two listed authors have equal contribution. Author ordering is determined by coin flip
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.16972 Zobrazit plný text záznamu View this record from Arxiv