https://github.com/aranbarri/time-series-forecasting
A lightweight app to load time series data, forecast the future using Facebook Prophet, and visualize results with Streamlit.
https://github.com/aranbarri/time-series-forecasting
ai forecast forecasting prophet prophet-facebook prophet-model python-ai streamlit time-series
Last synced: 2 months ago
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A lightweight app to load time series data, forecast the future using Facebook Prophet, and visualize results with Streamlit.
- Host: GitHub
- URL: https://github.com/aranbarri/time-series-forecasting
- Owner: aranbarri
- License: mit
- Created: 2025-05-30T09:58:10.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-01T14:19:45.000Z (4 months ago)
- Last Synced: 2025-06-09T09:45:25.201Z (4 months ago)
- Topics: ai, forecast, forecasting, prophet, prophet-facebook, prophet-model, python-ai, streamlit, time-series
- Language: Python
- Homepage:
- Size: 36.1 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📈 Time Series Forecasting App
A simple app to load time series data, forecast the future using Facebook Prophet, and visualize results with Streamlit.
## Run with Docker
```bash
docker-compose up -d
```
Once running, open your browser at [http://localhost:8888](http://localhost:8888)
## 📄 Expected CSV Format
The uploaded CSV file must contain at least:
- `ds`: Date column in YYYY-MM-DD format
- One or more numeric columns to be selected as the target for forecasting
### Example:
```
ds,sales,temperature
2024-01-01,123,15.2
2024-01-02,150,16.8
2024-01-03,170,14.9
```
You will be able to choose which numeric column (e.g., `sales`) to forecast.
## Forecast Options
- Select the target column for prediction from your dataset
- Choose how many days to forecast (from 7 to 90 days)
- View results as an interactive time series plot
## Project Structure
```
time-series-forecasting/
├── app/
│ ├── main.py # Streamlit app
│ ├── model.py # Forecasting logic using Prophet
│ └── utils.py # CSV loading and preprocessing
├── Dockerfile # Container build instructions
├── docker-compose.yml # Docker service definition
├── requirements.txt # Python dependencies
├── .gitignore # Ignored files and folders
└── README.md # Project documentation
```
## Features
- Upload your own time series CSV file
- Select a numeric column to forecast
- Adjust forecast length from 7 to 90 days
- Forecast the future using Prophet
- Interactive plot with Streamlit
- Fully containerized with Docker
## Technologies
- Python 3.11
- [Prophet](https://facebook.github.io/prophet/)
- [Streamlit](https://streamlit.io/)
- Docker
## License
MIT License. Feel free to use and adapt.
## Screenshots
