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Ensemble and regression algorithms for stock market forecasting in pre-pandemic and pandemic scenarios
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Orientador(es)
Resumo(s)
Understanding and forecasting stock prices can be extremely profitable for people and useful
for governments, specially in pandemic and pre-pandemic scenarios. The development of a
machine learning model that can predict market behavior is the ultimate challenge. Therefore,
many studies have been applying machine learning models in the context of stock prices
forecasting. Model and feature selection is extremely important for any machine learning
problem, so comparative studies are extremely important. In this work a comparative study is
developed to evaluate tuned ensemble and regression models for predicting future stock
prices.
Descrição
Palavras-chave
Stock market Forecasting Regression Ensemble Machine learning