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- W4383670033 abstract "The stock market has become a very important part of the world’s economy, and the accurate prediction of stock prices has gained significant attention over the years. Predicting accurate stock prices can help an individual make sound financial investment decisions and reduce the risk involved in investing in stock markets. This paper proposes a Recurrent Neural Network based stock price prediction framework using two very popular deep learning models, namely the Gated Recurrent Unit (GRU) and the Bidirectional Long Short-Term Memory (BiLSTM) models, and compares the performances of both of these models in predicting future stock price [1]. Experimental results conducted by us show that both the GRU and BiLSTM models can be used to accurately predict future stock prices. The RMSE and MAE evaluation metrics for both the GRU and BiLSTM models were calculated by tuning the hyperparameters and comparing the performances of both models to predict future stock prices. The experiments were carried out using a freely available dataset of stock market, which contained the stock prices of various companies. It was observed that the BiLSTM model performed better than the GRU model. However, GRU was almost twice as fast as the BiLSTM model in predicting stock prices due to its simpler architecture. Both models provide accurate results and can be very effective in predicting future stock market prices." @default.
- W4383670033 created "2023-07-09" @default.
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- W4383670033 date "2023-01-01" @default.
- W4383670033 modified "2023-09-23" @default.
- W4383670033 title "Stock Price Prediction Using GRU and BiLSTM Models" @default.
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- W4383670033 doi "https://doi.org/10.1007/978-981-99-0483-9_9" @default.
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