Research map / Time series / Foundation models for time series
ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ShapeNet instead of competing MTSC methods | MTSC on UEA Archive · 30 UEA datasets | accuracy | Helped |
| Use cluster-wise triplet sampling instead of random triplet sampling (ablation) | ShapeNet · ArticularyWordRecognition, Epilepsy, RacketSports, UWaveGestureLibrary | accuracy | Helped |
| Select final shapelets with the utility-based clustering method instead of random selection (ablation) | ShapeNet · ArticularyWordRecognition, Epilepsy, RacketSports, UWaveGestureLibrary | accuracy | Helped |
| Increase the number of selected shapelets from 5 to 50 (ablation) | ShapeNet · ArticularyWordRecognition, Epilepsy, RacketSports, UWaveGestureLibrary | accuracy | Helped |
| Increase the number of selected shapelets beyond 50 (ablation) | ShapeNet · ArticularyWordRecognition, Epilepsy, RacketSports, UWaveGestureLibrary | accuracy | Hurt |
| Use cluster-wise triplet loss instead of original triplet loss (ablation) | Mdc-CNN shapelet embedding · ArticularyWordRecognition | training loss convergence | Helped |
Closest papers by meaning
- ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models (NeurIPS 2025 · similarity 0.91)
- Learning Soft Sparse Shapes for Efficient Time-Series Classification (ICML 2025 · similarity 0.90)
- Diffusion Language-Shapelets for Semi-supervised Time-Series Classification (AAAI 2024 · similarity 0.90)
- Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification (NeurIPS 2024 · similarity 0.88)
- MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification (AAAI 2025 · similarity 0.85)
- TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning (ICML 2024 · similarity 0.85)
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