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.
- 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.
See requirements.txt for dependencies.
For detailed theory on time series forecasting, see Documentation.md.
See team_details.txt for team information.
The application is deployed on Render and can be accessed at: https://time-series-forecasting-3xih.onrender.com
- Clone the repository.
- Install dependencies using
pip install -r requirements.txt. - Run the application with
python app.py. - Open a web browser and navigate to
http://127.0.0.1:5000/.
Contributions are welcome. Please submit a pull request with your changes.
This project is licensed under the MIT License. See LICENSE for details.