Sparse Variational Gaussian Process with Dynamic Kernel for Electricity Demand Forecasting

Author:

Eressa Muluken Regas1,Badis Hakim1,George Laurent1,Grosso Dorian2

Affiliation:

1. Gustave Eiffel University, LIGM (UMR 8049), ESIEE Paris,Marne-la-Vallée,France,F-77454

2. METRON - Energy Intelligence for Industries 30 Rue de Gramont,Paris,75002

Publisher

IEEE

Reference34 articles.

1. Markov chain monte carlo and variational inference: Bridging the gap;salimans;International Conference on Machine Learning,2015

2. Variational model selection for sparse gaussian process regression;titsias,2009

3. Variational inference for gaussian process models with linear complexity;cheng;arXiv preprint arXiv 1711 11575,2017

4. Variational learning of inducing variables in sparse gaussian processes;titsias;Artificial Intelligence and Statistics,2009

5. Scalable gaussian process regression using deep neural networks;huang;Twenty-Fourth International Joint Conference on Artificial Intelligence,2015

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1. Scalable and Interpretable Forecasting of Hydrological Time Series Based on Variational Gaussian Processes;Water;2024-07-15

2. Random Sparse Approximation for a Fast and Scalable Gaussian Process;2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE);2022-12-22

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