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    Forecast of LSTM-XGBoost in Stock Price Based on Bayesian Optimization

    Tian Liwei1,2,*, Feng Li1, Sun Yu3, Guo Yuankai4

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 855-868, 2021, DOI:10.32604/iasc.2021.016805

    Abstract The prediction of the “ups and downs” of stock market prices is one of the important undertakings of the financial market. Since accurate prediction helps foster considerable economic benefits, stock market prediction has attracted significant interest by both investors and researchers. Efforts into building an accurate, stable and effective model to predict stock prices’ movements have been proliferating at a fast pace, to meet such a challenge. Firstly, this paper uses a correlation analysis to analyze the attributes of a stock dataset, processing missing values, determining the data attributes to be retained data, then divide… More >

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