This project demonstrates the application of the Holt-Winters (Triple Exponential Smoothing) model for time series forecasting. The project includes:
- Decomposition of time series into trend, seasonal, and residual components.
- Visualization of monthly and quarterly seasonality.
- Analysis using ACF and PACF plots.
- Model evaluation with metrics like MAE, RMSE, and MAPE.
- File:
airmiles.csv - Description: Contains monthly air passenger miles data.
- Frequency: Monthly data.
- Seasonal decomposition with
seasonal_decompose. - Visualization of trends and seasonal patterns.
- Implementation of the Holt-Winters method for forecasting.
- Model evaluation using multiple error metrics.
- Python 3.8 or higher
- Libraries:
matplotlib,pandas,statsmodels,scikit-learn
- Clone the repository:
git clone https://github.com/AdhamKhouly/holt-winters-forecasting-challenge.git cd holt-winters-forecasting-challenge