Enhancing financial product forecasting accuracy using EMD and feature selection with ensemble models

Enhancing financial product forecasting accuracy using EMD and feature selection with ensemble models

This study examines the impact of Empirical Mode Decomposition (EMD) and Recursive Feature Elimination (RFE) on the prediction of financial product performance employing several ensemble machine learning models, including Random Forest, XGBoost, LightGBM, AdaBoost, CatBoost, Bagging, and ExtraTrees....

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Bibliographic Details
Main Authors: Eddy Suprihadi, Nevi Danila, Zaiton Ali
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Journal of Open Innovation: Technology, Market and Complexity
Subjects:
Financial Forecasting
Ensemble Learning
Time Series Prediction
Empirical Mode Decomposition (EMD)
Feature Selection (RFE)
Online Access:http://www.sciencedirect.com/science/article/pii/S2199853125000666
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