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A Hybrid Framework Using PCA, EMD and LSTM Methods for Stock Market Price Prediction with Sentiment Analysis

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References

2008Surveying stock market forecasting techniques – Part II: Soft computing methods841 cited
2017Can Twitter Help Predict Firm-Level Earnings and Stock Returns?606 cited
2020Evaluating time series forecasting models: an empirical study on performance estimation methods521 cited
2016Forecasting daily stock market return using dimensionality reduction395 cited
2020Stock market prediction using machine learning classifiers and social media, news336 cited
2021A systematic review of stock market prediction using machine learning and statistical techniques208 cited
2017Can investor sentiment be a momentum time-series predictor? Evidence from China165 cited
2011A novel text mining approach to financial time series forecasting134 cited
2021Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM)134 cited
2018Deep Leaming for Stock Market Prediction Using Technical Indicators and Financial News Articles119 cited
2018Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression80 cited
2022Effective machine learning model combination based on selective ensemble strategy for time series forecasting69 cited

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2025Multi-feature stock price prediction by LSTM networks based on VMD and TMFG41 cited

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