---
title: "INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification"
authors:
  - "Seongjun Lee"
  - "Seokhyun Lee"
  - "Changhee Lee"
doi: "10.24963/ijcai.2026/496"
url: "https://ijcai.org/proceedings/2026/496"
pdf: "https://ijcai.org/proceedings/2026/0496.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "4456-4464"
track: "Main Track"
keywords:
  - "Machine Learning: Feature extraction, selection and dimensionality reduction"
  - "Machine Learning: Classification"
  - "Machine Learning: Time series and data streams"
  - "Machine Learning: Explainable/Interpretable machine learning"
---

# INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification

Seongjun Lee, Seokhyun Lee, Changhee Lee

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 4456-4464. DOI: [10.24963/ijcai.2026/496](https://doi.org/10.24963/ijcai.2026/496)

## Abstract

Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of time-series classification (TSC) and to make model decision-making processes more transparent. 
However, existing methods primarily focus on population-level shapelets optimized across the entire dataset, which leads to two fundamental limitations: (i) population-level patterns often misalign with instance-specific features, resulting in suboptimal performance and potentially misleading interpretations, and (ii) most methods treat shapelets as independent entities, overlooking important temporal dependencies and interactions among multiple patterns.
To address these limitations, we propose INSHAPE, an interpretable TSC framework that discovers variable-length, discriminative temporal patterns specific to each time series.
INSHAPE identifies these patterns as non-overlapping segments and models their temporal dependencies, thereby providing clear instance-level interpretations while achieving strong predictive performance. 
Furthermore, INSHAPE bridges local and global interpretability through a bottom-up approach, aggregating instance-level shapelets into prototypical (population-level) shapelets. 
Extensive experiments on 128 UCR and 30 UEA benchmark datasets show that INSHAPE consistently outperforms state-of-the-art shapelet-based methods while providing more intuitive and interpretable insights.