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Modeling of time series using random forests: theoretical developments

De Richard Davis

Apparaît dans la collection : New Results on Time Series and their Statistical Applications / Séries chronologiques: nouveaux résultats et applications statistiques

In this paper we study asymptotic properties of random forests within the framework of nonlinear time series modeling. While random forests have been successfully applied in various fields, the theoretical justification has not been considered for their use in a time series setting. Under mild conditions, we prove a uniform concentration inequality for regression trees built on nonlinear autoregressive processes and, subsequently, use this result to prove consistency for a large class of random forests. The results are supported by various simulations. (This is joint work with Mikkel Slot Nielsen.)

Informations sur la vidéo

Date de captation 14/09/2020
Date de publication 09/10/2020
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs
Réalisateur(s) Guillaume Hennenfent
Format MP4

Données de citation

DOI 10.24350/CIRM.V.19654803
Citer cette vidéo Davis, Richard (14/09/2020). Modeling of time series using random forests: theoretical developments. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19654803
URL https://dx.doi.org/10.24350/CIRM.V.19654803

Domaine(s)

Bibliographie

DAVIS, Richard A. et NIELSEN, Mikkel S. Modeling of time series using random forests: theoretical developments. arXiv preprint arXiv:2008.02479, 2020. - https://arxiv.org/abs/2008.02479

Codes MSC

Document(s)

https://www.cirm-math.fr/RepOrga/2233/Slides/Davis-CIRM2020.pdf

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