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    Stock-Price Forecasting Based on XGBoost and LSTM

    Pham Hoang Vuong1, Trinh Tan Dat1, Tieu Khoi Mai1, Pham Hoang Uyen2, Pham The Bao1,*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 237-246, 2022, DOI:10.32604/csse.2022.017685

    Abstract Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we… More >

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