Multiscale Bootstrap Correction for Random Forest Voting: A Statistical Inference Approach to Stock Index Trend Prediction

This paper proposes a novel multiscale random forest model for stock index trend prediction, incorporating statistical inference principles to improve classification confidence. Traditional random forest classifiers rely on majority voting, which can yield biased estimates of class probabilities, es...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Aizhen Ren, Yanqiong Duan, Juhong Liu
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2025-11-01
Colecção:	Mathematics
Assuntos:	multiscale bootstrap method random forest stock index forecasting
Acesso em linha:	https://www.mdpi.com/2227-7390/13/22/3601
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!