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Time Series Domain Adaptation via Sparse Associative Structure Alignment
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| align segment-length distributions across domains with Lα (ablation) | SASA · Air quality forecast | RMSE: 30.15 -> 29.54 | Helped |
| align sparse associative structures across domains with Lβ (ablation) | SASA · Air quality forecast | RMSE: 30.39 -> 29.54 | Helped |
| align segment-length distributions across domains with Lα (ablation) | SASA · In-hospital mortality prediction (MIMIC-III) | AUC (%): 78.47 -> 79.16 | Helped |
| align sparse associative structures across domains with Lβ (ablation) | SASA · In-hospital mortality prediction (MIMIC-III) | AUC (%): 78.20 -> 79.16 | Helped |
Closest papers by meaning
- CauDiTS: Causal Disentangled Domain Adaptation of Multivariate Time Series (ICML 2024 · similarity 0.90)
- Enhancing Multivariate Time-Series Domain Adaptation via Contrastive Frequency Graph Discovery and Language-Guided Adversary Alignment (AAAI 2025 · similarity 0.87)
- Boosting Transferability and Discriminability for Time Series Domain Adaptation (NeurIPS 2024 · similarity 0.87)
- Large Pre-trained time series models for cross-domain Time series analysis tasks (NeurIPS 2024 · similarity 0.85)
- TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts (AAAI 2025 · similarity 0.85)
- Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement (ICLR 2025 · similarity 0.85)
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