https://github.com/knowusuboaky/forecasting_models
Advanced Time Series Forecasting Suite: Leveraging Diverse Models for Predictive Analytics
Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (15.4%) to scientific vocabulary
Repository
Advanced Time Series Forecasting Suite: Leveraging Diverse Models for Predictive Analytics
Basic Info
- Host: GitHub
- Owner: knowusuboaky
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://pypi.org/project/forecasting-models/
- Size: 55.7 KB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
Advanced Time Series Forecasting Suite: Leveraging Diverse Models for Predictive Analytics
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:
python
pip install forecasting_models
Usage
Import the desired model from the package and use it in your project. For example:
python
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
Owner
- Login: knowusuboaky
- Kind: user
- Repositories: 1
- Profile: https://github.com/knowusuboaky
GitHub Events
Total
- Issues event: 1
- Push event: 4
- Pull request event: 1
- Create event: 1
Last Year
- Issues event: 1
- Push event: 4
- Pull request event: 1
- Create event: 1
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 1
- Total pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: 3 minutes
- Total issue authors: 1
- Total pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: 3 minutes
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- knowusuboaky (1)
Pull Request Authors
- knowusuboaky (2)
Top Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 150 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 1
- Total maintainers: 1
pypi.org: forecasting-models
Advanced Time Series Forecasting Suite: Leveraging Diverse Models for Predictive Analytics
- Homepage: https://github.com/knowusuboaky/forecasting_models
- Documentation: https://forecasting-models.readthedocs.io/
- License: MIT License
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Latest release: 0.1
published over 2 years ago