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BILCO: An Efficient Algorithm for Joint Alignment of Time Series
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add bidirectional pushing to HIPR (BI-HIPR) (ablation) | HIPR · synthetic joint alignment, large kappa | running time: nearly 4 times faster | Helped |
| use ELCO (linear component operations) instead of generic push-relabel (ablation) | HIPR · synthetic joint alignment, large kappa | running time: nearly 4 times faster | Helped |
| combine ELCO with bidirectional pushing (BILCO) (ablation) | HIPR · synthetic joint alignment, largest kappa | running time: 16 folds -> around 100-fold | Helped |
| add bidirectional pushing to ELCO (BILCO vs ELCO) (ablation) | ELCO · synthetic joint alignment, varying kappa | running time | Helped |
| increase hyperparameter kappa | most max-flow methods · synthetic joint alignment | running time | Hurt |
| BILCO vs best peer method on increasing graph size | BILCO · synthetic image signal propagation | running time: at least 10 times faster | Helped |
| BILCO vs peers memory on synthetic graphs | BILCO · synthetic image signal propagation | memory usage: 1/10 memory | Helped |
| BILCO vs peers on signal propagation | BILCO · calcium imaging signal propagation | running time: 18 to 50 times faster, averaging 29 | Helped |
| BILCO vs peers on depth from stereo | BILCO · pendulum1*, artroom2* | running time: 9.8 -> 1 relative to IBFS (x9.8 to x10.0) | Helped |
| BILCO vs peers on signature identification | BILCO · real signatures and forgeries | running time: x15.2 and x20.9 versus IBFS | Helped |
| BILCO vs peers on full-size data | BILCO · Glia Ca2+, pendulum1, artroom2 | memory feasibility | Helped |
| specialized memory layout using coordinate order | BILCO · all datasets | memory usage: 8|V| + 4|E| bytes vs 48|V| + 64|E| for IBFS | Helped |
Closest papers by meaning
- TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning (ICML 2025 · similarity 0.86)
- AliO: Output Alignment Matters in Long-Term Time Series Forecasting (NeurIPS 2025 · similarity 0.84)
- Align-RAG: Alignment Is All You Need for TSFM In-Context Learning (NeurIPS 2026 · similarity 0.83)
- TimeBase: The Power of Minimalism in Efficient Long-term Time Series Forecasting (ICML 2025 · similarity 0.82)
- PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation (NeurIPS 2026 · similarity 0.82)
- Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series (ICLR 2025 · similarity 0.82)
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