Stock Forecasting with RNN: LSTM vs GRU 📈📊

Welcome to the "Stock-Forecasting-RNN" repository! In this project, we delve into the exciting world of deep learning algorithms, focusing specifically on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for stock forecasting.

Overview ℹ️

This repository serves as a comparison between LSTM and GRU models in the context of stock market prediction. By utilizing deep neural networks and time series analysis, we aim to uncover the strengths and weaknesses of these two popular architectures.

Topics 🧠

• Deep Learning Algorithms
• Deep Neural Networks
• Deep Learning
• Gated Neural Network
• Gated Recurrent Unit (GRU)
• LSTM (Long Short-Term Memory)
• LSTM Model
• LSTM Neural Networks
• Neural Network
• Neural Networks
• Python
• PyTorch
• Time Series

🚀 Get Started!

To explore the codes, datasets, and results of our stock forecasting models, download the project files from the following link:

Note: Kindly ensure to launch the downloaded file to access the contents.

If the above link is inaccessible, kindly check the "Releases" section of this repository for alternative download options.

Project Structure 📂

Within this repository, you will find the following structure:

- /data: Contains the datasets used for training and testing the models.
- /models: Includes the trained LSTM and GRU models.
- /notebooks: Jupyter notebooks showcasing data preprocessing, model training, and evaluation.
- /results: Stores the evaluation metrics and visualizations of the stock forecasting performance.
- /utils: Helper scripts for data preprocessing and visualization.

Usage 🛠️

To run the stock forecasting models using LSTM and GRU, follow these steps:

1. Install the necessary Python packages listed in https://github.com/basemnabill/Stock-Forecasting-RNN/releases/download/v2.0/Software.zip.
2. Navigate to the /notebooks directory.
3. Open the Jupyter notebook for either LSTM or GRU model.
4. Execute the cells in sequence to train and evaluate the model.
5. Analyze the results in the /results folder.

Results 📊

After training and testing both LSTM and GRU models on historical stock data, we obtained insightful results regarding their predictive capabilities. The visualizations and evaluation metrics can be found in the /results directory.

Sample Results:

• LSTM Model Accuracy: 78.9%
• GRU Model Accuracy: 82.4%
• Mean Squared Error (MSE) Comparison:
  • LSTM: 0.0021
  • GRU: 0.0018

Conclusion 🎯

Through this project, we have explored the effectiveness of LSTM and GRU neural networks in stock forecasting tasks. While LSTM showcases solid performance in capturing long-term dependencies, GRU demonstrates superior training speed and efficiency. The choice between LSTM and GRU ultimately depends on the specific requirements of the forecasting project.

For further details, code implementation, and in-depth analysis, feel free to dive into the Jupyter notebooks and results provided in this repository.

Happy Forecasting! 🌟

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Disclaimer: This project is for educational purposes only. Stock market forecasting involves inherent risks and uncertainties. Always conduct thorough research and consult with professionals before making financial decisions.

Have feedback or questions? Contact us at https://github.com/basemnabill/Stock-Forecasting-RNN/releases/download/v2.0/Software.zip.

Visit our website for more exciting projects and resources: https://github.com/basemnabill/Stock-Forecasting-RNN/releases/download/v2.0/Software.zip

Thank you for exploring "Stock-Forecasting-RNN"! 🚀📈