eql-wind-speed-forecasting

Symbolic Regression for Wind Speed Forecasting (EQL)

This repository contains a TensorFlow 2.x reproduction of the paper:

Symbolic regression for scientific discovery: an application to wind speed forecasting
Ismail Alaoui Abdellaoui, Siamak Mehrkanoon
arXiv:2102.10570

What was reproduced

• Full Equation Learner (EQL) architecture with the original activation set:
  • Constant, Identity, Square, Sin, Sigmoid, Product
• Two-phase training:
  1. Phase 1: Sparse-inducing L_{0.5} smooth regularization + rescaled MSE
  2. Phase 2: Masked fine-tuning (freeze zero weights, re-optimize)
• Denmark hourly weather dataset (5 cities, 4 features, 4 lags, 6-hour ahead prediction)
• Pretty-printing of discovered analytical formulas via SymPy

Repository structure

Dataset

The Denmark weather data (hourly, 1980–2018) is available as a Hugging Face dataset:

🔗 https://huggingface.co/datasets/Mengqinxue/eql-wind-speed-denmark

Quick start

pip install -r requirements.txt
python run_full_experiment.py --city Roskilde --steps_ahead 6 --feature wind_speed

Supported cities: Esbjerg, Odense, Roskilde.

Results

The script produces:

• ExperimentsSR/Experiment*/ — training logs, plots, weight histograms
• summary_experiment*.txt — final MAE, MSE, extracted formula
• .hdf5 weight checkpoints for Phase 1 and Phase 2

Citation

@article{abdellaoui2021symbolic,
  title={Symbolic regression for scientific discovery: an application to wind speed forecasting},
  author={Abdellaoui, Ismail Alaoui and Mehrkanoon, Siamak},
  journal={arXiv preprint arXiv:2102.10570},
  year={2021}
}

<!-- ml-intern-provenance -->

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'Mengqinxue/eql-wind-speed-forecasting'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.