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].
Use the package manager pip to install kernel_quantile_regression.
pip install kernel-quantile-regressionfrom 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)-
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.
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plots/ Plots for the tutorial and experiments.
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src/kernel_quantile_regression Source code.
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train_test scripts to train the models, saved and test them.
- models contains , for each quantile, the pickled trained models.
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utils Utility functions for extracting, loading and transforming raw data of the GEFCom2014 challenge.
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kqr_tutorial.py Getting started example, where our method is compared against other valid quantile regressors.
[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