Clustering traffic flows with histogram, ACF, PSD or naive representations improves traffic matrix prediction over global models on Abilene and GÉANT data, with most gains at moderate cluster counts and similar accuracy across representations.
Bridging the gap: A decade review of time-series clustering methods
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abstract
Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensional time-series data, however, posing significant challenges for analyzing latent structures over extended temporal scales. Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. While previous surveys have focused on specific methodological categories, we bridge the gap between traditional clustering methods and emerging deep learning-based algorithms, presenting a comprehensive, unified taxonomy for this research area. This survey highlights key developments and provides insights to guide future research in time-series clustering.
years
2026 2representative citing papers
FMMVCC combines Mamba-based encoders with multi-view contrastive learning and fuzzy clustering to achieve state-of-the-art univariate time series clustering with linear computational complexity.
citing papers explorer
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On the Role of Time Series Clustering in Traffic Matrix Prediction
Clustering traffic flows with histogram, ACF, PSD or naive representations improves traffic matrix prediction over global models on Abilene and GÉANT data, with most gains at moderate cluster counts and similar accuracy across representations.
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FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series
FMMVCC combines Mamba-based encoders with multi-view contrastive learning and fuzzy clustering to achieve state-of-the-art univariate time series clustering with linear computational complexity.