# Forecasting Models Library
## Overview
The `forecasting_models` library is a comprehensive Python package designed for time series forecasting. It integrates various robust forecasting methodologies, making it an ideal tool for applications in finance, supply chain management, weather prediction, and more. This library is perfect for analysts, data scientists, and developers who seek efficient and accurate forecasting solutions.
## Features
- **Prophet Model Integration**: Leverages Facebook's Prophet model for forecasting univariate time series data with strong seasonal patterns.
- **XGBoost Model**: Employs the XGBoost algorithm for its high performance in machine learning.
- **Random Forest Model**: Incorporates the Random Forest algorithm, a popular method for ensemble learning.
- **MLP (Multi-Layer Perceptron) Regressor**: Implements a neural network-based approach for complex data patterns.
- **Gradient Boosting Model**: Offers a Gradient Boosting Regressor, effective for various data irregularities.
## Installation
To install the package, run the following command:
```bash
pip install forecasting_models
```
## Usage
Import the desired model from the package and use it in your project. For example:
```bash
from forecasting_models import generateProphetForecast
from forecasting_models import generateXGBoostForecast
from forecasting_models import generateRandomForestForecast
from forecasting_models import generateMLPForecast
from forecasting_models import generateGradientBoostingForecast
```
## Ideal Use Cases
- Detailed time series analysis and forecasting.
- Rapid prototyping for research and development projects.
- Educational purposes for understanding different forecasting techniques.
## Contributing
We welcome contributions, suggestions, and feedback to make this library even better. Feel free to fork the repository, submit pull requests, or open issues.
## Documentation & Examples
For documentation and usage examples, visit the GitHub repository: https://github.com/knowusuboaky/forecasting_models
**Author**: Kwadwo Daddy Nyame Owusu - Boakye\
**Email**: kwadwo.owusuboakye@outlook.com\
**License**: MIT
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"description": "# Forecasting Models Library\r\n\r\n## Overview\r\nThe `forecasting_models` library is a comprehensive Python package designed for time series forecasting. It integrates various robust forecasting methodologies, making it an ideal tool for applications in finance, supply chain management, weather prediction, and more. This library is perfect for analysts, data scientists, and developers who seek efficient and accurate forecasting solutions.\r\n\r\n## Features\r\n- **Prophet Model Integration**: Leverages Facebook's Prophet model for forecasting univariate time series data with strong seasonal patterns.\r\n- **XGBoost Model**: Employs the XGBoost algorithm for its high performance in machine learning.\r\n- **Random Forest Model**: Incorporates the Random Forest algorithm, a popular method for ensemble learning.\r\n- **MLP (Multi-Layer Perceptron) Regressor**: Implements a neural network-based approach for complex data patterns.\r\n- **Gradient Boosting Model**: Offers a Gradient Boosting Regressor, effective for various data irregularities.\r\n\r\n## Installation\r\nTo install the package, run the following command:\r\n\r\n```bash\r\npip install forecasting_models\r\n```\r\n\r\n## Usage\r\nImport the desired model from the package and use it in your project. For example:\r\n\r\n```bash\r\nfrom forecasting_models import generateProphetForecast\r\nfrom forecasting_models import generateXGBoostForecast\r\nfrom forecasting_models import generateRandomForestForecast\r\nfrom forecasting_models import generateMLPForecast\r\nfrom forecasting_models import generateGradientBoostingForecast\r\n```\r\n\r\n## Ideal Use Cases\r\n- Detailed time series analysis and forecasting.\r\n- Rapid prototyping for research and development projects.\r\n- Educational purposes for understanding different forecasting techniques.\r\n\r\n## Contributing\r\nWe welcome contributions, suggestions, and feedback to make this library even better. Feel free to fork the repository, submit pull requests, or open issues.\r\n\r\n## Documentation & Examples\r\nFor documentation and usage examples, visit the GitHub repository: https://github.com/knowusuboaky/forecasting_models\r\n\r\n**Author**: Kwadwo Daddy Nyame Owusu - Boakye\\\r\n**Email**: kwadwo.owusuboakye@outlook.com\\\r\n**License**: MIT\r\n",
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