This project is on the web service implementation of a series of research projects that pioneer the tourism demand forecasting using modern AI approaches. Related implementations and publications:
- https://github.com/tulip-lab/open-code
- https://www.sciencedirect.com/science/article/abs/pii/S0160738319300143
The project aims to implement a web service using ML flow (or other package, if suitable) to cover the pipeline of tourism demand forecasting, including:
- Data Crawling (univariate and multivariate)
- Forecasting model integration, based on released code
- Forecasting model evaluation, walkthrough evaluation based on the latest data, and historical data
- Web service deployment and security configuration
- Online documents and demonstrations
- Machine Learning, Python Programming.
- Python Programming
- ML Flow
- GitHub Repository
- CloudFlare
- Markdown document
- Python programming, data science, machine learning, pattern recognition, ML Flow, GitHub Repository
There will be a varies of specific technique skills for the team members:
- Data crawling;
- ML Flow platform;
- Time Series Forecasting model
- Cloud platform (Cloudflare)
- Documentation
- GUI
No special hardware, and common open packages only.