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This paper introduces FMMVCC, a novel deep clustering framework designed for univariate time series data that utilizes Mamba-based state space sequence modeling to efficiently capture long-range temporal dependencies with linear complexity. By incorporating multi-view self-supervised learning techniques, including temporal masking and augmentations, FMMVCC significantly enhances the clustering performance without the need for extensive annotations. Experimental results across 15 benchmark datasets demonstrate that FMMVCC outperforms existing state-of-the-art methods, achieving the best performance in 29 out of 60 evaluations and securing the highest average rank across all scenarios tested.
FMMVCC achieves unprecedented clustering performance for univariate time series by efficiently capturing long-range dependencies with linear complexity.
In many realistic scenarios, large volumes of time series data are generated with limited or expensive annotations. This limitation makes supervised learning methods difficult to apply and leads to the use of unsupervised approaches capable of discovering meaningful structures directly from raw data. Clustering therefore plays a crucial role in organizing time series into groups that share similar temporal patterns, enabling exploratory analysis and downstream tasks without requiring manual labeling. However, existing deep clustering methods often struggle to capture long-range temporal dependencies or rely on architectures with high computational cost. This paper introduces FMMVCC, a Mamba-based deep clustering framework for time series that leverages state space sequence modeling to efficiently learn temporal representations with linear complexity. Additionally, it utilizes multi-view self-supervised learning with temporal masking and augmentations. Experimental evaluation in 15 benchmark datasets proves that FMMVCC consistently outperforms state-of-the-art baselines, achieving the best overall performance in 29 of 60 total metric evaluations and the highest average rank in all tested scenarios.
