Official repository for Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels.

This repository contains Python implementations of Sequential Feature Detachment (SFD) for feature selection and Detach-ROCKET for time-series classification. Developed primarily in Python and utilizing NumPy, scikit-learn, and sktime libraries, the core functionalities are encapsulated within the following classes:

• DetachRocket: Detach-ROCKET model class. It is constructed by pruning an initial ROCKET, MiniRocket or MultiROCKET model using SFD and selecting the optimal size.

• DetachMatrix: Class for applying Sequential Feature Detachment to any dataset matrix structured as (n_instances, n_features).

For a detailed explanation of the model and methods please refer to the article.

To install the required dependencies, execute:

pip install numpy scikit-learn sktime pyts
pip install git+https://github.com/gon-uri/detach_rocket --quiet

The model usage is the same as in the scikit-learn library.

# Instantiate Model
DetachRocketModel = DetachRocket('rocket', num_kernels=10000)

# Trian Model
DetachRocketModel.fit(X_train,y_train)

# Predict Test Set
y_pred = DetachRocketModel.predict(X_test)

For univariate time series, the shape of X_train should be (n_instances, n_timepoints).

For multivariate time series, the shape of X_train should be (n_instances, n_variables, n_timepoints).

Detailed usage examples can be found in the included Jupyter notebooks in the examples folder.

• Built-in support for multilabel classification (DONE!).
• Pytorch implementation of ROCKET, MiniRocket or MultiROCKET.

This project is licensed under the BSD-3-Clause License.

If you find these methods useful in your research, please cite the article:

APA

Uribarri, G., Barone, F., Ansuini, A., & Fransén, E. (2023). Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels. arXiv preprint arXiv:2309.14518.

BIBTEX

@article{uribarri2023detach,
  title={Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels},
  author={Uribarri, Gonzalo and Barone, Federico and Ansuini, Alessio and Frans{\'e}n, Erik},
  journal={arXiv preprint arXiv:2309.14518},
  year={2023}
}