QSMP: finding representative time series subsequences through Quick Shift+Matrix Profile
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Computer Science > Machine Learning
Title:QSMP: finding representative time series subsequences through Quick Shift+Matrix Profile
Abstract:Finding representative waveforms in long time series has scientific and practical value in many domains, as it enables summarization and visualization of large time series datasets, and downstream tasks like classification and forecasting. We present here QSMP, a method to find representative waveforms in long time series through a density-guided clustering of time series subsequences. Our method makes a novel connection between Quick Shift, a mode-seeking algorithm, and the Matrix Profile, a time series similarity-search data structure, to adapt Quick Shift to the clustering of subsequences in long time series, with a space complexity that is superior to the state-of-the-art method. Our experiments on synthetic and real datasets show that QSMP can be a valuable tool to summarize and visualize long time series by finding representative waveforms.
| Comments: | Accepted for publication in the 2026 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
| Subjects: | Machine Learning (cs.LG) |
| ACM classes: | I.5.3; I.5.4 |
| Cite as: | arXiv:2608.15492 [cs.LG] |
| (or arXiv:2608.15492v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.15492
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Carlos Mendoza-Cardenas [view email][v1] Sun, 16 Aug 2026 02:45:25 UTC (1,904 KB)
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