Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework
This addresses the challenge of clustering complex time series data for domains like finance, healthcare, and environmental monitoring, offering incremental improvements over existing techniques.
The paper tackles the problem of clustering time series data by proposing the Variational Mixture Graph Autoencoder (VMGAE), which uses graph-based embeddings to capture temporal dependencies, and it significantly outperforms state-of-the-art methods in experiments.
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.