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Research Paper | Neural Networks | India | Volume 6 Issue 4, April 2017
Predicting Stock Prices Using LSTM
Murtaza Roondiwala | Harshal Patel | Shraddha Varma
Abstract: The art of forecasting the stock prices has been a difficult task for many of the researchers and analysts. In fact, investors are highly interested in the research area of stock price prediction. For a good and successful investment, many investors are keen in knowing the future situation of the stock market. Good and effective prediction systems for stock market help traders, investors, and analyst by providing supportive information like the future direction of the stock market. In this work, we present a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) approach to predict stock market indices.
Keywords: Long short-term memory LSTM, recurrent neural network RNN, nifty 50, root mean square error RMSE, prediction, stock prices
Edition: Volume 6 Issue 4, April 2017,
Pages: 1754 - 1756
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Research Paper, Neural Networks, India, Volume 6 Issue 8, August 2017
Pages: 1196 - 1200Rainfall Forecasting using Neural Network Fitting Tool (NFTOOL)
Bhavika R. Panchasara | Falguni P. Parekh
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Research Paper, Neural Networks, Saudi Arabia, Volume 8 Issue 4, April 2019
Pages: 500 - 508Neural Network and Multiple Regression Models for PM2.5 Prediction in Rabigh, Saudi Arabia: A Comparative Assessment
Issam Mohammed Aquil Alghanmi | Ibrahim Abdelaziz | Al-Darrab | Osman Imam Taylan | Omar Seraj Aburizaiza