kernel-quantile-regression 0.0.13

Kernel quantile regression

Kernel quantile regression

The kernel_quantile_regression package is an open source implementation of the quantile regressor techique introduced in [1].

Example of kernel quantile regression on the Melbourne temperature data [2].

Installation

Use the package manager pip to install kernel_quantile_regression.

pip install kernel-quantile-regression

Usage

from kernel_quantile_regression.kqr import KQR

# create model instance
# specify your quantile q and hyperparameters C and gamma
kqr_1=KQR(alpha=q, C=100, gamma=0.5)

# train model
kqr_1.fit(X_train, y_train)

# predict
kqr_1.predict(X_test)

Repo files

• Data/ The Data directory contains the raw files for the GEFCom2014 challenge [3], data can be accessed from Dr. Tao Hong blog http://blog.drhongtao.com/2017/03/gefcom2014-load-forecasting-data.html. The Data folder contains also the transformed raw data, those constitute the input for our probabilistic forecasting study.

• plots/ Plots for the tutorial and experiments.

• src/kernel_quantile_regression Source code.

• train_test scripts to train the models, saved and test them.

  • models contains , for each quantile, the pickled trained models.

• utils Utility functions for extracting, loading and transforming raw data of the GEFCom2014 challenge.

• kqr_tutorial.py Getting started example, where our method is compared against other valid quantile regressors.

References

[1] Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, and Alexander J. Smola. 2006. Non- parametric Quantile Estimation. Journal of Machine Learning Research 7, 45 (2006), 1231–1264. https://www.jmlr.org/papers/volume7/takeuchi06a/takeuchi06a.pdf

[2] Rob J Hyndman, David M Bashtannyk, and Gary K Grunwald. 1996. Estimating and visualizing conditional densities. Journal of Computational and Graphical Statistics 5, 4 (1996), 315–336. https://www.jstor.org/stable/1390887

[3] Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli, and Rob J.Hyndman. 2016b. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. International Journal of Forecasting 32, 3 (2016), 896–913. https://www.sciencedirect.com/science/article/abs/pii/S0169207016000133?via%3Dihub

License

MIT