A gradient boosting approach to the Kaggle load forecasting competition

By Souhaib Ben Taieb, Rob J Hyndman. 2014

294 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/11cc72c0ade933a1ea20ea37d31829420a9bc5c3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ayça Deniz
2. Sven F. Crone
3. Tansel Dökeroğlu
4. Hakan Ezgi Kiziloz

In Electrical and Electronic Engineering

1. Sven F. Crone
2. Tansel Dökeroğlu
3. Devon K. Barrow

In Management Science and Operations Research

1. Devon K. Barrow
2. Sven F. Crone
3. Tansel Dökeroğlu
4. Christoph Bergmeir
5. Kasun Bandara
6. Slawek Smyl

Citations in the corpus

• A comprehensive survey on recent metaheuristics for feature selection (2022, background): “There are over 100 thousand public datasets as of November 2021 [271].”
• A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting (2016, unknown): “Examples include K -nearest neighbor (KNN) [6], [7], neural networks (NNs) [8]–[10], support vector machines [11], boosting [12], and fuzzy NNs [13]–[15].”
• A comparison of AdaBoost algorithms for time series forecast combination (2016, background): “These approaches have had some success in time series forecasting e.g. the gradient boosting approach of Taieb and Hyndman (2014) which ranked 5th out of 105 participating teams in the Kaggle load forecasting competition.”