ADD GRU Model For Stock Price Prediction

[deepanshubaghel]

Is this a unique feature?

• I have checked "open" AND "closed" issues and this is not a duplicate

Is your feature request related to a problem/unavailable functionality? Please describe.

Yes, the current stock price prediction module only uses the LSTM model, and there is no implementation of a GRU model. GRUs (Gated Recurrent Units) can offer a more computationally efficient alternative to LSTMs while delivering similar prediction performance, making them ideal for time series tasks like stock price forecasting. Adding a GRU model would allow us to compare results with LSTM and potentially improve prediction efficiency and accuracy.

Proposed Solution

Implement a GRU model alongside the existing LSTM for stock price prediction, allowing for a comparative analysis in terms of accuracy and efficiency. This addition will expand the module’s flexibility and improve performance, with clear documentation on GRU vs. LSTM selection for users.

Screenshots

No response

Do you want to work on this issue?

No

If "yes" to above, please explain how you would technically implement this (issue will not be assigned if this is skipped)

To implement the GRU model, I would modify the current LSTM-based code by replacing the LSTM layers with GRU layers using tf.keras.layers.GRU from TensorFlow/Keras. The GRU model would use similar configurations, such as dropout layers for regularization and dense layers for the output. I would reference the existing LSTM code structure for consistency, adjusting hyperparameters for GRU as needed. Finally, I would add functionality to compare GRU and LSTM results, documenting any performance differences.

[github-actions]

👋 Thanks for opening this issue! We appreciate your contribution. Please make sure you’ve provided all the necessary details and screenshots, and don't forget to follow our Guidelines and Code of Conduct. Happy coding! 🚀