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Summary

  • mirror the latest standalone UR2CUTE estimator: wire BaseModel knobs, enable padding/sanity overrides, and add auto-threshold bookkeeping + masked BCE/regression losses
  • tighten inference by using the learned threshold and exposing a _return_components flag so training/validation can consume both CNN outputs safely
  • skip validation when val_size == 0 to keep Lightning happy on short datasets and add pytest smoke coverage (fit/predict + auto-threshold)

Testing

  • python -m pytest tests/test_models/test_ur2cute.py -q --cov-fail-under=0

sina and others added 3 commits October 24, 2025 21:01
- Add UR2CUTE (Using Repetitively 2 CNNs for Unsteady Timeseries Estimation)
- Two-step hurdle approach: Classification CNN + Regression CNN
- Specialized for intermittent/sparse time series (>30% zeros)
- Inherits from BaseModel with full NeuralForecast integration
- Comprehensive documentation and docstrings
- Reference: Mirshahi et al. (2024) MENDEL Journal"
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✅ FH-Prevail
❌ sina


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@FH-Prevail
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FH-Prevail commented Nov 18, 2025

Hi together,
What are the steps I should do at this moment?
for detail about the method , I already published it in a journal here:
https://www.mendel-journal.org/index.php/mendel/article/view/413

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2 participants