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

• URL: http://arxiv.org/abs/2503.24019v1
• Date: Mon, 31 Mar 2025 12:46:33 GMT
• Title: AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
• Authors: Keshav Das, Julie Keisler, Margaux Brégère, Amaury Durand,
• Abstract summary: Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout.<n> Reliable short-term forecasts may be obtained by combining a Generalized Additive Models (GAM) with a State-Space model.<n>This work generalizes it for automated online generalized additive model selection by defining an efficient modeling of the search space.
• Score: 0.34952465649465553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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