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- W4313444406 abstract "The factors affecting the stock market are large in numbers that make accurate predictions a challenging task. There is an overwhelming addition of data on the Internet, and some of this data like current market sentiments along with technical indicators can help in better stock prediction. In this paper, the state-of-the-art machine learning models ARIMA, SVR, LSTM, and XGBoost along with the ensembles of these models using weighted averaging and boosting techniques have been studied and compared on a 1-year and 5-year timeline. The data have been collected for two companies, HDFC and Sun Pharma, listed in the Nifty 50 stocks on the National Stock Exchange (NSE) in India.. The study shows the ensemble usefulness for different models, technical indicators, and sentimental analysis (Wikipedia hits and Google News mentions) in the stock prediction." @default.
- W4313444406 created "2023-01-06" @default.
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- W4313444406 date "2023-01-01" @default.
- W4313444406 modified "2023-10-01" @default.
- W4313444406 title "Stock Market Prediction Using Ensemble Learning and Sentimental Analysis" @default.
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- W4313444406 doi "https://doi.org/10.1007/978-981-19-5868-7_32" @default.
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