{"id":28410641,"url":"https://github.com/aranbarri/time-series-forecasting","last_synced_at":"2025-07-30T06:12:54.479Z","repository":{"id":296377207,"uuid":"993152817","full_name":"aranbarri/time-series-forecasting","owner":"aranbarri","description":"A lightweight app to load time series data, forecast the future using Facebook Prophet, and visualize results with Streamlit.","archived":false,"fork":false,"pushed_at":"2025-06-01T14:19:45.000Z","size":37,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-09T09:45:25.201Z","etag":null,"topics":["ai","forecast","forecasting","prophet","prophet-facebook","prophet-model","python-ai","streamlit","time-series"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aranbarri.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-30T09:58:10.000Z","updated_at":"2025-06-01T14:19:49.000Z","dependencies_parsed_at":"2025-05-30T13:18:41.923Z","dependency_job_id":"d67ea716-9973-4f7e-8e4a-a12f5a0285ab","html_url":"https://github.com/aranbarri/time-series-forecasting","commit_stats":null,"previous_names":["aranbarri/time-series-forecasting"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/aranbarri/time-series-forecasting","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aranbarri%2Ftime-series-forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aranbarri%2Ftime-series-forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aranbarri%2Ftime-series-forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aranbarri%2Ftime-series-forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aranbarri","download_url":"https://codeload.github.com/aranbarri/time-series-forecasting/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aranbarri%2Ftime-series-forecasting/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261197023,"owners_count":23123634,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","forecast","forecasting","prophet","prophet-facebook","prophet-model","python-ai","streamlit","time-series"],"created_at":"2025-06-02T12:08:26.677Z","updated_at":"2025-07-30T06:12:54.461Z","avatar_url":"https://github.com/aranbarri.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📈 Time Series Forecasting App\n\n\nA simple app to load time series data, forecast the future using Facebook Prophet, and visualize results with Streamlit.\n\n## Run with Docker\n\n```bash\ndocker-compose up -d\n```\n\nOnce running, open your browser at [http://localhost:8888](http://localhost:8888)\n\n![image](https://github.com/user-attachments/assets/de51f08c-9eb3-4321-b55e-8292db004564)\n\n![image](https://github.com/user-attachments/assets/350e66c7-a4b8-405f-8aa5-c0b97af02e01)\n\n\n## 📄 Expected CSV Format\n\nThe uploaded CSV file must contain at least:\n\n- `ds`: Date column in YYYY-MM-DD format\n- One or more numeric columns to be selected as the target for forecasting\n\n### Example:\n\n```\nds,sales,temperature\n2024-01-01,123,15.2\n2024-01-02,150,16.8\n2024-01-03,170,14.9\n```\n\nYou will be able to choose which numeric column (e.g., `sales`) to forecast.\n\n## Forecast Options\n\n- Select the target column for prediction from your dataset\n- Choose how many days to forecast (from 7 to 90 days)\n- View results as an interactive time series plot\n\n## Project Structure\n\n```\ntime-series-forecasting/\n├── app/\n│   ├── main.py        # Streamlit app\n│   ├── model.py       # Forecasting logic using Prophet\n│   └── utils.py       # CSV loading and preprocessing\n├── Dockerfile         # Container build instructions\n├── docker-compose.yml # Docker service definition\n├── requirements.txt   # Python dependencies\n├── .gitignore         # Ignored files and folders\n└── README.md          # Project documentation\n```\n\n## Features\n\n- Upload your own time series CSV file\n- Select a numeric column to forecast\n- Adjust forecast length from 7 to 90 days\n- Forecast the future using Prophet\n- Interactive plot with Streamlit\n- Fully containerized with Docker\n\n## Technologies\n\n- Python 3.11\n- [Prophet](https://facebook.github.io/prophet/)\n- [Streamlit](https://streamlit.io/)\n- Docker\n\n## License\n\nMIT License. Feel free to use and adapt.\n\n\n\n## Screenshots\n\n\n![image](https://github.com/user-attachments/assets/31754d41-fd2d-4a7b-9609-0e9de67cb009)\n\n![image](https://github.com/user-attachments/assets/c4d7c703-d56c-424e-9272-7609613625b3)\n\n![image](https://github.com/user-attachments/assets/5a31a636-dd8e-4c00-bcf8-8e18f2d28a7b)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faranbarri%2Ftime-series-forecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faranbarri%2Ftime-series-forecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faranbarri%2Ftime-series-forecasting/lists"}