A powerful and interactive web application for forecasting time series data—specifically stock closing prices—using three robust models: ARIMA, Prophet, and LSTM. The app is built using Streamlit and leverages live data from Yahoo Finance, along with additional datasets available on Kaggle.

• Overview
• Features
• Tech Stack
• Data Source
• Installation
• Usage
• Project Structure
• Customization
• Future Improvements
• Contributing
• License

The Time Series Forecasting App is designed to help users explore and forecast stock closing prices using three forecasting models:

• ARIMA: A classical time series model that is ideal for linear patterns.
• Prophet: Developed by Facebook, this model is robust to missing data and handles seasonality and trend changes efficiently.
• LSTM: A deep learning model implemented in TensorFlow, capable of capturing complex non-linear patterns in time series data.

Users can interact with the app via a user-friendly dashboard, configure model parameters, and view both static and interactive charts. Additional features include seasonal decomposition analysis and the ability to download forecast results as a CSV file.

• Model Selection: Choose between ARIMA, Prophet, and LSTM models.
• Data Sourcing: Fetch live stock data from Yahoo Finance.
• Customizable Forecast Parameters: Adjust forecasting periods and model-specific parameters.
• Interactive Visualization: Toggle between static (Matplotlib) and interactive (Plotly) charts.
• User-Selectable Date Range: Zoom in on historical data with user-defined date ranges.
• Seasonal Decomposition: View the trend, seasonal, and residual components of your time series.
• Download Forecast: Export forecast results as a CSV file.
• Custom Theming: Enhanced user interface with custom CSS styling for a polished look.

• Python 3.8+
• Streamlit: For building interactive web applications.
• Pandas & NumPy: For data manipulation and numerical operations.
• yfinance: For downloading historical stock data.
• Matplotlib & Plotly: For data visualization.
• Statsmodels: For ARIMA and seasonal decomposition.
• Prophet: For robust forecasting with trend and seasonality.
• TensorFlow & Keras: For building and loading LSTM models.
• scikit-learn: For data preprocessing.

• Yahoo Finance: The primary source for live stock data is Yahoo Finance via the yfinance library.
• Kaggle Dataset (Optional): Alternatively, you can experiment with datasets such as Historical Stock Prices.

1. Clone the Repository:

   git clone https://github.com/yourusername/time-series-forecasting-app.git
   cd time-series-forecasting-app

2. Create a Virtual Environment:

   python -m venv venv

3. Activate the virtual environment:

• On Windows:

  venv\Scripts\activate

• On macOS/Linux:

  source venv/bin/activate

4. Install Dependencies:

   pip install -r requirements.txt

• Run the App:

• In your project directory, start the Streamlit app with:

  streamlit run app.py

1. Interact with the Dashboard:

• Use the sidebar to select the ticker, date range, forecast period, and model-specific parameters.
• Adjust the visualization date range to zoom in on the desired window.
• View interactive or static charts depending on your selection.
• Access additional analysis such as seasonal decomposition.
• Download the forecasted results as a CSV file using the provided download button.

time-series-forecasting-app/ │ ├── app.py # Main Streamlit application file ├── lstm_model.h5 # Pre-trained LSTM model (if applicable) ├── scaler.pkl # Preprocessing scaler for LSTM model (if applicable) ├── requirements.txt # List of required Python packages └── README.md # Project documentation (this file)

Contributions are welcome! If you have suggestions, bug fixes, or improvements, please open an issue or submit a pull request. Follow these steps:

• Fork the repository.
• Create a new branch: git checkout -b feature/YourFeatureName
• Commit your changes: git commit -am 'Add some feature'
• Push to the branch: git push origin feature/YourFeatureName
• Open a pull request.