CT-Miner: Fast and Coarse-Grained Time-Series Pattern Mining via Cartesian Trees
Time series often contain recurring structural patterns, and efficiently mining such patterns into compact representations is essential for scalable analysis of long sequences. Cartesian tree (CT) equivalence provides a well-established structural abstraction that preserves hierarchical order structure while discarding exact values and fine-grained ordinal variations. By grouping multiple ordinal patterns into a shared structural form, CT equivalence offers a principled way to compress recurring temporal structure. However, mining frequent CT-equivalent patterns at scale remains computationally expensive. A naive pairwise approach repeatedly constructs and counts CT representations over s...
| Authors | Hyundong Jin, Hyunki Hong, Yo-Sub Han |
|---|---|
| Primary category | cs.DS |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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