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- W3213461418 abstract "In recent years, there has been a predominant use of Artificial Neural Networks (ANN) to solve real-world problems. One such conundrum is the prediction of stock prices since they are convoluted, sporadic, and dynamic. This study focuses on the use of RNN, the Recurrent Neural Networks to identify and forecast trends that stock prices will follow using based on historical data. The LSTM and GRU models are considered for developing prediction models of stock prices. These models do not have the vanishing gradient problem, unlike traditional RNN. Both models are compared and analyzed to find a model that would predict satisfactory prices with maximum accuracy which can help a trader to make efficient decisions." @default.
- W3213461418 created "2021-11-22" @default.
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- W3213461418 date "2021-07-06" @default.
- W3213461418 modified "2023-10-18" @default.
- W3213461418 title "A Deep Learning Approach for Prediction of Stock Price Based on Neural Network Models: LSTM and GRU" @default.
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- W3213461418 doi "https://doi.org/10.1109/icccnt51525.2021.9579782" @default.
- W3213461418 hasPublicationYear "2021" @default.
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