6 papers
Combining Weather Forecast Aggregation and State-Space Models for Adaptive Probabilistic Electricity Load Forecasting
Joseph de Vilmarest, Jonathan Dumas, Jean Thorey
Accurate electricity load forecasting is essential to ensure the real-time balance between supply and demand, especially in systems increasingly influenced by weather conditions an…
Assessing Extrapolation of Peaks Over Thresholds with Martingale Testing
Joseph de Vilmarest, Olivier Wintenberger
We present the winning strategy for the EVA2025 Data Challenge, which aimed to estimate the probability of extreme precipitation events. These events occurred at most once in the d…
Adaptive time series forecasting with markovian variance switching
Baptiste Abélès, Joseph de Vilmarest, Olivier Wintemberger
Adaptive time series forecasting is essential for prediction under regime changes. Several classical methods assume linear Gaussian state space model (LGSSM) with variances constan…
Online Learning Approach for Survival Analysis
Camila Fernandez, Pierre Gaillard, Joseph de Vilmarest +1
We introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data. This framework enables the estimation…
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models
Guillaume Lambert, Bachir Hamrouche, Joseph de Vilmarest
We focus on day-ahead electricity load forecasting of substations of the distribution network in France; therefore, our problem lies between the instability of a single consumption…
Adaptive Probabilistic Forecasting of Electricity (Net-)Load
Joseph de Vilmarest, Jethro Browell, Matteo Fasiolo +2
Electricity load forecasting is a necessary capability for power system operators and electricity market participants. The proliferation of local generation, demand response, and e…