Stochastic Online Convex Optimization; Application to probabilistic time series forecasting - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2021

Stochastic Online Convex Optimization; Application to probabilistic time series forecasting

Résumé

Stochastic regret bounds for online algorithms are usually derived from an "online to batch" conversion. Inverting the reasoning, we start our analyze by a "batch to online" conversion that applies in any Stochastic Online Convex Optimization problem under stochastic exp-concavity condition. We obtain fast rate stochastic regret bounds with high probability for non-convex loss functions. Based on this approach, we provide prediction and probabilistic forecasting methods for non-stationary unbounded time series.
Fichier principal
Vignette du fichier
SOCO.pdf (244.47 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03125863 , version 1 (29-01-2021)
hal-03125863 , version 2 (26-04-2021)
hal-03125863 , version 3 (25-02-2022)
hal-03125863 , version 4 (31-03-2023)

Identifiants

Citer

Olivier Wintenberger. Stochastic Online Convex Optimization; Application to probabilistic time series forecasting. 2021. ⟨hal-03125863v1⟩
154 Consultations
171 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More