155 citations · 356 across the 34 of their papers we have counts for
6 papers · 1 filter
Integrating Complex Covariate Transformations in Generalized Additive Models
Claudia Collarin, Matteo Fasiolo, Yannig Goude +1
Transformations of covariates are widely used in applied statistics to improve interpretability and to satisfy assumptions required for valid inference. More broadly, feature engin…
Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
Xiuqin Xu, Ying Chen, Yannig Goude +1
Probabilistic forecasting of electricity load curves is of fundamental importance for effective scheduling and decision making in the increasingly volatile and competitive energy m…
qgam: Bayesian non-parametric quantile regression modelling in R
Matteo Fasiolo, Simon N. Wood, Margaux Zaffran +2
Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.…
Scalable visualisation methods for modern Generalized Additive Models
Matteo Fasiolo, Raphaël Nedellec, Yannig Goude +1
In the last two decades the growth of computational resources has made it possible to handle Generalized Additive Models (GAMs) that formerly were too costly for serious applicatio…
Fast calibrated additive quantile regression
M. Fasiolo, S. N. Wood, M. Zaffran +2
We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation…
Modelling and forecasting daily electricity load curves: a hybrid approach
Haeran Cho, Yannig Goude, Xavier Brossat +1
We propose a hybrid approach for the modelling and the short-term forecasting of electricity loads. Two building blocks of our approach are (i) modelling the overall trend and seas…