https://github.com/visvav/time-series-forecasting
A web-based application for analyzing historical weather data and forecasting future trends using advanced statistical models. Built with Python and Flask, the app provides interactive visualizations, stationarity tests, and predictions using ARIMA and SARIMA models.
https://github.com/visvav/time-series-forecasting
acf-pacf adf-kpss arima-model html python sarima-model time-series time-series-analysis weather-forecast
Last synced: 3 months ago
JSON representation
A web-based application for analyzing historical weather data and forecasting future trends using advanced statistical models. Built with Python and Flask, the app provides interactive visualizations, stationarity tests, and predictions using ARIMA and SARIMA models.
- Host: GitHub
- URL: https://github.com/visvav/time-series-forecasting
- Owner: VisvaV
- License: mit
- Created: 2025-03-18T20:19:09.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-13T08:57:10.000Z (about 1 year ago)
- Last Synced: 2025-06-10T11:44:31.695Z (about 1 year ago)
- Topics: acf-pacf, adf-kpss, arima-model, html, python, sarima-model, time-series, time-series-analysis, weather-forecast
- Language: Python
- Homepage:
- Size: 6.23 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Time-Series Forecasting
This project is a comprehensive time series forecasting application built using Python and Flask. It utilizes various statistical models like ARIMA and SARIMA to predict temperature trends. The application includes a web interface to visualize and forecast temperature data.
## Features
- **Time Series Visualization**: Plots time series data for better understanding.
- **Moving Averages**: Calculates Simple, Cumulative, and Exponential Moving Averages.
- **ACF/PACF Plots**: Displays Autocorrelation and Partial Autocorrelation plots.
- **ARIMA/SARIMA Forecasting**: Predicts future temperature values using ARIMA and SARIMA models.
- **Stationarity Tests**: Performs Augmented Dickey-Fuller and KPSS tests for stationarity.
- **Metrics Calculation**: Computes Mean Absolute Error, Root Mean Squared Error, and Mean Absolute Percentage Error.
## Requirements
See [requirements.txt](requirements.txt) for dependencies.
## Documentation
For detailed theory on time series forecasting, see [Documentation.md](Documentation.md).
## Team Details
See [team_details.txt](team_details.txt) for team information.
## Deployment
The application is deployed on Render and can be accessed at:
[https://time-series-forecasting-3xih.onrender.com](https://time-series-forecasting-3xih.onrender.com)
## Usage
1. Clone the repository.
2. Install dependencies using `pip install -r requirements.txt`.
3. Run the application with `python app.py`.
4. Open a web browser and navigate to `http://127.0.0.1:5000/`.
## Contributing
Contributions are welcome. Please submit a pull request with your changes.
## License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.