# AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting

arXiv: https://arxiv.org/abs/2503.24019 · HTML: https://mlsanity.com/paper/2503.24019/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models](https://mlsanity.com/paper/2404.16233/) (2024)
- [Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France](https://mlsanity.com/paper/2009.06527/) (2020)
- [An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters](https://mlsanity.com/paper/2303.12797/) (2023)
- [Automated Deep Learning for Load Forecasting](https://mlsanity.com/paper/2405.08842/) (2024)
- [A Bandit Approach with Evolutionary Operators for Model Selection](https://mlsanity.com/paper/2402.05144/) (2024)
- [Efficient mid-term forecasting of hourly electricity load using generalized additive models](https://mlsanity.com/paper/2405.17070/) (2024)
- [Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models](https://mlsanity.com/paper/2302.08192/) (2023)
- [Automated Spatio-Temporal Weather Modeling for Load Forecasting](https://mlsanity.com/paper/2409.16326/) (2024)

## Abstract (arXiv)

Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout. Reliable short-term forecasts may be obtained by combining a Generalized Additive Models (GAM) with a State-Space model (Obst et al., 2021), leading to an adaptive (or online) model. A GAM is an over-parameterized linear model defined by a formula and a state-space model involves hyperparameters. Both the formula and adaptation parameters have to be fixed before model training and have a huge impact on the model's predictive performance. We propose optimizing them using the DRAGON package of Keisler (2025), originally designed for neural architecture search. This work generalizes it for automated online generalized additive model selection by defining an efficient modeling of the search space (namely, the space of the GAM formulae and adaptation parameters). Its application to short-term French electricity demand forecasting demonstrates the relevance of the approach