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- W4300530952 abstract "Profitable stock price prediction is becoming increasingly popular in today's business environment. A major aspect in the expansion of shareholders in a business's stock is the prediction of stock price movements, which can boost the enthusiasm of speculators in putting their money into the company. Obtaining an accurate forecast for the future value of a company could result in a significant profit. In the past few years, various methodologies have been used to forecast the direction of the stock market. Thus, in this research, we present a distinct approach for stock price estimation model which includes the popular model called as the long short-term memory (LSTM) model. In conclusion, simulation results show, it can be concluded that our suggested scheme may accurately estimate future stock trends by employing various RNN models, such as the LSTM, with appropriate hyper-parameter tuning. It was necessary to experiment with different numbers of hidden layers, epochs, thick layers and modules of the system used by the hidden layers so as to develop a more accurate model that could be used to estimate future stock values with more precision. The evaluations are carried out with the help of a freely available data set of stock market prices that includes the opening and closing values as well as high and low." @default.
- W4300530952 created "2022-10-03" @default.
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- W4300530952 date "2022-01-01" @default.
- W4300530952 modified "2023-10-14" @default.
- W4300530952 title "An Improved Stock Market Index Prediction System Based on LSTM" @default.
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- W4300530952 doi "https://doi.org/10.1007/978-981-19-1324-2_15" @default.
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