STOCK PRICE PREDICTION USING GRID HYPER PARAMETER TUNING IN GATED RECURRENT UNIT
DOI:
https://doi.org/10.29121/ijoest.v6.i3.2022.345Keywords:
Machine Learning, Gated Recurrent Unit, Back Propagation, Neural Networks, Hyper Parameter TuningAbstract
Nowadays people are using social media to show their talent, to voice their viewpoint to society, etc. The use of social media has drastically grown during and after pandemic. Since, the power of social media is known to us, it would be beneficial to invest in such trending companies. But, understanding market pattern will be required to get maximum benefit from stock market, otherwise it may lead to losses. Machine learning is an essential tool for predicting such tasks. Here deep learning based Gated Recurrent Unit neural network is used for prediction. To develop optimized model, grid search algorithm is used for Gated Recurrent Unit hyper parameter tuning. Also, the hyper parameter values obtained by the model was used to verify and predict stock prices for other companies.
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Copyright (c) 2022 Bhavsar Shachi, Ravi Gor
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