TimeLSTM

An interactive Streamlit application for multi-step time series forecasting using LSTM (Long Short-Term Memory) networks. Designed for both data science professionals and non-technical users, this project makes deep learning–powered forecasting accessible, customizable, and intuitive.

✨ Features

• 📂 Data Handling

  • Upload CSV files with auto date-detection & mixed datatype support.

• 🧹 Preprocessing

  • Handles missing values, categorical encoding, and feature scaling.

• 📊 Exploration & Visualization

  • Interactive time series plots, histograms, correlation heatmaps, and seasonal decomposition.

• 🧠 LSTM Model

  • Customizable architecture (layers, neurons, forecast horizon) with GPU acceleration.

• ⚡ Training Framework

  • Adjustable epochs, batch size, and learning rate with real-time monitoring.

• 📈 Results Analysis

  • MSE, RMSE, MAE metrics, residual error inspection, and forecast visualization.

• 💾 Export & Deployment

  • Save results as CSV/plots, persistent model storage for reuse.

⚙️ How It Works

1. Streamlit UI – Provides an interactive web-based interface for model training and forecasting.

2. Data Pipeline – Upload, preprocess, and visualize datasets before training.

3. LSTM Training – Uses PyTorch to train customizable models with GPU support.

4. Evaluation – Generates forecast plots, error metrics, and residual analysis.

5. Export Options – Save trained models, forecasts, and plots for future use.

🌍 Use Cases

• 💹 Finance – Stock price prediction (multi-day horizon).

• 🔌 Energy – Electricity demand forecasting for smart grids.

• 🛒 Retail – Product demand prediction for inventory optimization.

• 🏥 Healthcare – Patient admission forecasting with seasonal trends.

🛠️ Requirements

• Python 3.8+

• Streamlit

• PyTorch

• Pandas, scikit-learn, statsmodels, Plotly

Install dependencies:

pip install -r requirements.txt

🚀 Getting Started

git clone https://github.com/Nyx1311/TimeLSTM.git cd TimeLSTM pip install -r requirements.txt streamlit run app.py

🤝 Contributing

Pull requests are welcome! For major changes, open an issue first to discuss improvements.

📜 License

This project is licensed under the MIT License.

Test app at https://timelstm.streamlit.app/