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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 for dependencies.

Documentation

For detailed theory on time series forecasting, see Documentation.md.

Team Details

See 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

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 for details.