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arXiv Machine Learning By Ivana Kesi\'c, Carolina Fortuna, Mihael Mohor\v{c}i\v{c}, Bla\v{z} Bertalani\v{c}

A Network Science Approach to Granular Time Series Segmentation

Read the original on arXiv Machine Learning →

The paper presents a method for dense univariate time‑series segmentation by treating each time point as a node in a graph and applying a Graph Attention Network (GAT). Using visibility‑based graph constructions, particularly WDPVG, the approach achieves competitive performance on a 59‑dataset benchmark, matching or surpassing traditional point‑wise classifiers while requiring far fewer parameters and less memory. The method’s advantage stems from long‑range edges that provide contextual information beyond local windows.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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