# Quantile Regression for Qualifying Match of GEFCom2017 Probabilistic Load Forecasting

arXiv: https://arxiv.org/abs/1809.03561 · HTML: https://mlsanity.com/paper/1809.03561/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties](https://mlsanity.com/paper/2106.06033/) (2021)
- [State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting](https://mlsanity.com/paper/2110.00334/) (2021)
- [Efficient mid-term forecasting of hourly electricity load using generalized additive models](https://mlsanity.com/paper/2405.17070/) (2024)

## Abstract (arXiv)

We present a simple quantile regression-based forecasting method that was applied in a probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data is log transformed and split into a long-term trend component and a remainder term. The key forecasting element is the quantile regression approach for the remainder term that takes into account weekly and annual seasonalities such as their interactions. Temperature information is only used to stabilize the forecast of the long-term trend component. Public holidays information is ignored. Still, the forecasting method placed second in the open data track and fourth in the definite data track with our forecasting method, which is remarkable given simplicity of the model. The method also outperforms the Vanilla benchmark consistently.