State-space models capture very well the evolution of the electricity demand; in particular, they perform well in the context of abrupt regime changes. The objective of Viking Conseil is to continue to develop this state-space methodology, while comparing it to other forecasting approaches.

Publications

• J. de Vilmarest and N. Werge (2024). An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition. International Journal of Forecasting.
• J. de Vilmarest and O. Wintenberger (2024). Viking: Variational Bayesian Variational Tracking. Statistical Inference for Stochastic Processes.
• J. de Vilmarest, J. Browell, M. Fasiolo, Y. Goude and O. Wintenberger (2023). Adaptive Probabilistic Forecasting of Electricity (Net-)Load. IEEE Transactions on Power Systems.
• G. Lambert, B. Hamrouche and J. de Vilmarest (2023). Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models. Nature Scientific Reports.
• J. de Vilmarest and Y. Goude (2022). State-Space Models for Online Post-Covid Electricity Load Forecasting Competition. IEEE Open Access Journal of Power and Energy.
• J. de Vilmarest and O. Wintenberger (2021). Stochastic online optimization using kalman recursion. Journal of Machine Learning Research.
• D. Obst, J. de Vilmarest and Y. Goude (2021). Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France. IEEE Transactions on Power Systems.

Preprints

• J. de Vilmarest and O. Wintenberger (2025). Assessing Extrapolation of Peaks Over Thresholds with Martingale Testing.
• N. Drobac, M. Brégère, J. de Vilmarest and O. Wintenberger (2025). Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting.
• J.L. Mahoromeza, A. Fermanian, J. de Vilmarest and O. Wintenberger (2025). Robust Estimation for Linear State-Space Models with Stochastic Covariates.
• B. Abélès, J. de Vilmarest and O. Wintenberger (2024). Adaptive time series forecasting with markovian variance switching.
• J. de Vilmarest and O. Wintenberger (2019). Logarithmic Regret for parameter-free Online Logistic Regression.

Competitions

• March – May 2025 : 1st place with Olivier Wintenberger at the EVA2025 Data Challenge.
• February 2022 – February 2023. Participation to the M6 Financial Forecasting Competition with Nicklas Werge (team AdaGaussMC_STU), focus on the Forecasts leaderboard. Awarded 3rd place during the 1st quarter and 5th global.
• June 2021. 1st place at the Competition on building energy consumption forecasting.
• March – April 2021. 1st place with Yannig Goude at the Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm.