https://github.com/iamfarrokhnejad/stock-forecasting-rnn

LSTM vs GRU
https://github.com/iamfarrokhnejad/stock-forecasting-rnn

deep-learning-algorithms deep-neural-networks deeplearning gated-neural-network gated-recurrent-unit gated-recurrent-units gru lstm lstm-model lstm-neural-networks neural-network neural-networks neuralnetwork neuralnetworks python pytorch time-series

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LSTM vs GRU

• Host: GitHub
• URL: https://github.com/iamfarrokhnejad/stock-forecasting-rnn
• Owner: IAmFarrokhnejad
• License: mit
• Created: 2025-01-17T15:56:19.000Z (9 days ago)
• Default Branch: main
• Last Pushed: 2025-01-17T16:09:05.000Z (9 days ago)
• Last Synced: 2025-01-17T17:24:10.908Z (9 days ago)
• Topics: deep-learning-algorithms, deep-neural-networks, deeplearning, gated-neural-network, gated-recurrent-unit, gated-recurrent-units, gru, lstm, lstm-model, lstm-neural-networks, neural-network, neural-networks, neuralnetwork, neuralnetworks, python, pytorch, time-series
• Language: Python
• Homepage:
• Size: 124 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Time Series Forecasting with LSTM and GRU Models

This repository contains Python code for time series forecasting using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks implemented in PyTorch. The models are trained and evaluated on a dataset named `amazon.csv`. (provided in this repository)

## Features

- **Model Architectures:** Includes LSTM and GRU implementations for sequence modeling.

- **Data Preprocessing:** Time series data is prepared with scaling, sequence generation, and train-validation splitting.

- **Model Training:** Comprehensive training loop with loss computation and model validation.

- **Performance Metrics:** Training and validation loss are reported for each epoch to track model performance.

## Requirements

- Python 3.8 or later

- PyTorch 2.0 or later

- NumPy

- Matplotlib

- scikit-learn

Install the required libraries using:

```bash

pip install torch numpy matplotlib scikit-learn

```

### Hardware

- A CUDA-compatible GPU is recommended for efficient training.

---

## Setup Instructions

1. **Clone the Repository**:

   ```bash

   git clone https://github.com/IAmFarrokhnejad/Stock-Forecasting-RNN

   ```

2. **Install Dependencies**:

   ```bash

   pip install torch torchvision matplotlib seaborn scikit-learn

   ```

3. **Prepare Your Dataset**:

- Ensure the amazon.csv file is placed in the root directory. The file should contain a single time series column for analysis.

- In case of changing the data directory, update the data variable path in the script to point to your dataset path.

## Running the Code

Execute the script to train both the LSTM and GRU models:

```bash

python stock-forecasting.py

```

The script will:

1. Train and validate models sequentially for EfficientNet, MobileNetV2, and ShuffleNet.

2. Save the classification report and confusion matrix for each model in the specified directory.

---

## Results

The models were trained for 40 epochs, and the following trends were observed:

1. LSTM:

- Training Loss decreased to 0.0001 by epoch 40.

- Validation Loss stabilized at 0.0016.

2. GRU:

- Training Loss decreased to 0.0001 by epoch 40.

- Validation Loss stabilized at 0.0019.

Both models demonstrate strong convergence, with GRU showing slightly slower validation loss improvement compared to LSTM.

---

## License

This project is licensed under the [MIT License](LICENSE).

---

## Author

[Morteza Farrokhnejad](https://github.com/IAmFarrokhnejad)

---

## Acknowledgments

- [PyTorch](https://pytorch.org/) for the deep learning framework.

- Matplotlib for visualization.