cfire - CFIRE v1.0 – A Reproducible and Interpretable Cross-Representation TSC Framework for Research

📦 CFIRE v1.0.0 – A Reproducible and Interpretable Cross-Representation TSC Framework for Research

CFIRE (Cross-Representation Feature Extraction) is a modular, interpretable, and reproducible framework designed for academic research in feature-based time series classification (TSC).

This release provides an extensible foundation for exploring diverse feature spaces across multiple signal representations, including time-domain statistics, frequency-domain coefficients, derivatives, wavelet transforms, and Hilbert-based features.

---

🧪 Key Research Features

• 🔁 Cross-representation pipeline: Time, frequency, DWT, FFT, derivative, and Hilbert transform support
  
• 🧠 Feature-rich foundation: Integrates Catch22 and TSFresh
  
• ⚙️ Parallelized extraction: Efficient multiprocessing for large-scale experimentation
  
• 📊 Classifier benchmarking: Includes ExtraTrees, XGBoost, Ridge, SVM, and more
  
• 🧩 Reproducibility and compatibility: Compatible with aeon and all UCR datasets

---

🛠 Included in v1.0

• crossfire.py: Core CFIRE implementation
  
• demo_.py: Example script for running experiments on any UCR dataset
  
• README.md: Setup guide, feature descriptions, usage, and citation instructions

---

🎯 Use CFIRE to:

• Benchmark and compare time series representations
• Evaluate interpretable, handcrafted feature sets
• Support TSC research with reproducible baselines
• Build and deploy robust models in low-resource or explainability-critical settings

---

📬 Contact

For academic inquiries, feedback, or collaborations, please reach out to:
📧 celal.alagoz@gmail.com

---

📖 Citation

If you use CFIRE in your research, please consider citing the repository.
@software{AlagozCFIREv100_2025, author = {Alagöz, Celal}, doi = {10.5281/zenodo.15695652}, month = jun, title = {{CFIRE v1.0.0 – A Reproducible and Interpretable Cross-Representation TSC Framework for Research}}, url = {https://github.com/alagoz/cfire}, version = {v1.0.0}, year = {2025} }

- Python
Published by alagoz about 1 year ago