Random forests for time-dependent processes - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue ESAIM: Probability and Statistics Année : 2020

Random forests for time-dependent processes

Résumé

Random forests were introduced by Breiman in 2001. We study theoretical aspects of both original Breiman’s random forests and a simplified version, the centred random forests. Under the independent and identically distributed hypothesis, Scornet, Biau and Vert proved the consistency of Breiman’s random forest, while Biau studied the simplified version and obtained a rate of convergence in the sparse case. However, the i.i.d hypothesis is generally not satisfied for example when dealing with time series. We extend the previous results to the case where observations are weakly dependent, more precisely when the sequences are stationary β−mixing.
Fichier principal
Vignette du fichier
ps180111.pdf (574.56 Ko) Télécharger le fichier
Origine : Publication financée par une institution

Dates et versions

hal-03022876 , version 1 (25-11-2020)

Identifiants

Citer

Benjamin Goehry. Random forests for time-dependent processes. ESAIM: Probability and Statistics, 2020, 24, pp.801-826. ⟨10.1051/ps/2020015⟩. ⟨hal-03022876⟩
75 Consultations
90 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More