Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark
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Computer Science > Machine Learning
Title:Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark
Abstract:This work presents a divide-and-conquer modeling strategy for the CTF-4-Science Lorenz benchmark, which evaluates chaotic-system prediction across twelve hidden scores and five scenario families: clean forecasting, noisy reconstruction, noisy-input forecasting, few-shot learning, and parametric generalization. Rather than forcing one model class to handle all regimes, the final system matched each prediction block to the evaluation behavior of its task group. The main contributions are: smoothing-based reconstruction for noisy full-trajectory denoising; NG-RC/NVAR models tuned for noisy long-time attractor forecasting; a fitted Lorenz transition correction restricted to the sensitive clean short-time prefix; and a parametric prefix blend for the interpolation task. The resulting system with final public score of 79.63 shows that bounded, scenario-specific updates can outperform broad model replacement on mixed chaotic forecasting benchmarks.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.10084 [cs.LG] |
| (or arXiv:2606.10084v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10084
arXiv-issued DOI via DataCite
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