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

TL;DR

INSHAPE introduces an instance-specific shapelet discovery framework for time-series classification, enhancing interpretability and predictive accuracy by modeling temporal dependencies and bridging local and global explanations.

Contribution

It proposes a novel method for discovering variable-length, instance-specific shapelets that account for temporal dependencies, improving interpretability and performance over population-level approaches.

Findings

Outperforms state-of-the-art shapelet methods on benchmark datasets.

Provides more intuitive and instance-specific interpretability.

Achieves strong predictive performance across diverse datasets.

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.