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...

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Bibliografiska uppgifter

Huvudupphov:	Aizhen Ren, Yanqiong Duan, Juhong Liu
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	MDPI AG 2025-11-01
Serie:	Mathematics
Ämnen:	multiscale bootstrap method random forest stock index forecasting
Länkar:	https://www.mdpi.com/2227-7390/13/22/3601
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