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shapelet-based time-series classification

techniqueactiveshapelet-based-time-series-classification-52022f4e·1 events·first seen May 20, 2026

Aliases: shapelet-based time-series classification

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UEA Time Series Classification Repository UCR Time Series Archive INSHAPE

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Probabilistic Time Series Forecasting UEA Time Series Classification Repository Connectionist Temporal Classification Graph Classification Selective Classification neural network image classifiers backpropagation through time multivariate time series representation learning Temporal Difference Learning scikit-learn Time Series Foundation Models time-series forecasting

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4arXiv · cs.AI·May 20, 2026·source ↗

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

INSHAPE is a new interpretable time-series classification framework that discovers variable-length discriminative temporal patterns specific to individual instances rather than across the full dataset population. It models temporal dependencies among non-overlapping segments and bridges local and global interpretability via a bottom-up aggregation into prototypical shapelets. Evaluated on 128 UCR and 30 UEA benchmark datasets, INSHAPE outperforms state-of-the-art shapelet-based methods while offering more intuitive instance-level explanations.

Evaluation and Benchmarking UEA Time Series Classification Repository shapelet-based time-series classification UCR Time Series Archive +1 more