edrft 0.1.0

RFT and edRFT models for significant wave-height time-series forecasting.

edRFT

edrft provides Random Vector Functional Link Transformer models for significant wave-height forecasting:

• RFTRegressor: a shallow randomized transformer encoder with ridge readout.
• EDRFTRegressor: an ensemble deep RFT with one output layer per hidden layer.
• Hyperopt/TPE tuning using the default edRFT search ranges.
• NDBC wave forecasting experiment helpers that do not write result artifacts.

The public package uses the model naming: RFT and edRFT. Older rft and edrft script names are retained only under legacy files for traceability.

Installation

Core install:

git clone https://github.com/statsdl/edRFT.git
cd edRFT
pip install .

Wave experiment dependencies:

pip install ".[wave]"

Development:

pip install -e ".[dev]"
pytest

Quick Start

import numpy as np
from edrft import EDRFTRegressor, make_forecasting_frame

series = np.sin(np.linspace(0, 16, 240))
X, y = make_forecasting_frame(series, order=4)

model = EDRFTRegressor(n_layers=3, n_hidden=32, random_state=0)
model.fit(X[:180], y[:180])
pred = model.predict(X[180:])

Wave Forecasting Example

python examples/run_wave_forecasting.py \
  --data-dir wave \
  --stations 46001h \
  --years 2017 \
  --seeds 0 \
  --look-back 48 \
  --horizon 4 \
  --layers 10 \
  --max-evals 100

The runner prints metrics to stdout only. It follows the original scripts:

• NDBC features: WDIR, WSPD, GST, APD, WVHT
• Missing sentinel cleanup
• Default look-back window: 48
• Default forecasting horizon: 4
• Min-max scaling to [-1, 1]
• Chronological split: 70% train, 10% validation, 20% test
• Hyperopt/TPE tuning with 100 evaluations by default
• Train+validation final fit
• RMSE, MAPE, MASE, and timing output

Hyperopt Tuning

from edrft.tuning import layerwise_tune_edrft

result = layerwise_tune_edrft(
    X,
    y,
    n_layers=10,
    validation_fraction=0.1 / 0.8,
    max_evals=100,
    random_state=0,
)

Repository Notes

Supported package code lives in src/edrft.

The legacy/, DeepRVFL_/, ForecastLib.py, and old experiment scripts are retained for traceability. They are not included in the PyPI wheel and are not the supported package API.

PyPI Release

The publish workflow uses PyPI Trusted Publishing. Configure the PyPI trusted publisher with:

• owner: statsdl
• repository: edRFT
• workflow: publish.yml
• environment: pypi

License

MIT

Reference

If you use edRFT in your work, please cite:

@article{bhambu2025deep,
  title={Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting},
  author={Bhambu, Aryan and Gao, Ruobin and Suganthan, Ponnuthurai Nagaratnam and Selvaraju, Natarajan},
  journal={Applied Soft Computing},
  pages={114136},
  year={2025},
  publisher={Elsevier}
}