5 citations · 10 across the 6 of their papers we have counts for
6 papers
Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting
Nina Drobac, Margaux Brégère, Joseph de Vilmarest +1
Nonlinear and delayed effects of covariates often render time series forecasting challenging. To this end, we propose a novel forecasting framework based on ridge regression with s…
An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition
Joseph de Vilmarest, Nicklas Werge
In this paper, we address the problem of probabilistic forecasting using an adaptive volatility method rooted in classical time-varying volatility models and leveraging online stoc…
State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting
Joseph de Vilmarest, Yannig Goude
We present the winning strategy of an electricity demand forecasting competition. This competition was organized to design new forecasting methods for unstable periods such as the…
Viking: Variational Bayesian Variance Tracking
Joseph de Vilmarest, Olivier Wintenberger
We consider the problem of time series forecasting in an adaptive setting. We focus on the inference of state-space models under unknown and potentially time-varying noise variance…
Stochastic Online Optimization using Kalman Recursion
Joseph de Vilmarest, Olivier Wintenberger
We study the Extended Kalman Filter in constant dynamics, offering a bayesian perspective of stochastic optimization. We obtain high probability bounds on the cumulative excess ris…
Logarithmic Regret for parameter-free Online Logistic Regression
Joseph De Vilmarest, Olivier Wintenberger
We consider online optimization procedures in the context of logistic regression, focusing on the Extended Kalman Filter (EKF). We introduce a second-order algorithm close to the E…