## 6 AOG Downtime Reduction and Implementation Results
### Holt-Winters Model
#### Logistic Regression  
The Logistic Regression model checks the significance of the predictors, including aircraft type, part failure rate, and compliance with maintenance schedules for AOG incidents. Nevertheless, its accuracy is relatively low, 48% ROC-AUC, meaning it is a non-informative classifier. The classification report shows that the model provides a very high accuracy in non-incident classification, hence showing a high imbalance in the classes in the dataset (Figure 23). Figure 21: Holt–Winters model. Figure 22: Confusion matrix. For instance, the model completely fails to recognize instances of class \"1\", with precision, recall, and F1-score stuck at zero for that category. While the overall accuracy appears to be 80%, this is driven entirely by correct predictions on the majority class \"0\". Consequently, the ROC-AUC score of about 0.48 below the 0.5 random-guess threshold illustrates that the model has poor discriminative ability. These metrics demonstrate an imbalance in predictions and suggest the need to reject this approach. This research evaluated three approaches--ARIMA, Holt-Winters, and Logistic Regression--to address the challenge of detecting Aircraft On-Ground (AOG) events. However, all models ultimately predicted every instance as \"No AOG\", as shown by confusion matrices with zero true positives for \"AOG\". While effective for general time series forecasting, the ARIMA model struggled with class imbalance and failed to identify minority class AOG events. Similarly, the Holt-Winters model, which accounts for trend and seasonality, also overlooked the \"AOG\" category, though it showed slight progress in forecasting seasonal patterns. Even with cost-sensitive adjustments, Logistic Regression could not sufficiently tune its parameters to capture the minority class. These findings highlight the critical need to address class imbalance in the dataset. Without properly balancing the data, models like ARIMA and Logistic Regression fail to detect actual AOG events, rendering their predictions ineffective. Despite its limited improvements, Holt-Winters' ability to handle trends and seasonality shows potential for further refinement. All the Variance Inflation Factor (VIF) gauges diagnose multicollinearity among predictors. The obtained VIF values below 1.5 demonstrate little multicollinearity in the regression model, stabilizing the coefficients. **Forecasting Approaches and Model Parameters Interpretation**  
ARIMA (Autoregressive Integrated Moving Average)  
- Parameters Used:  
tools identified significant correlations at the first lag, suggesting that a first-order autoregressive and moving average process would capture the data's dynamics. Differences were applied to ensure stationarity. - Performance Metrics:  
\(\bigcirc\)  MAPE: 54.9% (indicating moderate prediction errors). \(\bigcirc\)  RMSE: 3.19 (quantifying the scale of prediction inaccuracies). - Scientific Analysis: While ARIMA successfully captured short-term trends, it struggled to account for seasonality inherent in AOG incidents. This limitation led to higher MAPE and RMSE values. The model is better suited for datasets without strong cyclical patterns or those dominated by trend-based changes. - Parameters Used:  
\(\bigcirc\)  Alpha (Level Smoothing): 0.2  
\(\bigcirc\)  Beta (Trend Smoothing): 0.1  
\(\bigcirc\)  Gamma (Seasonal Smoothing): 0.1  
- Parameter Selection: These parameters were optimized to align with the moderate seasonality observed in the dataset. An additive method was selected, given that the seasonal variations appeared to have consistent amplitudes. - Performance Metrics:  
\(\bullet\)  MAPE: 45.65% (lower than ARIMA, indicating better accuracy). \(\bullet\)  RMSE: 2.64 (smaller error magnitude than ARIMA). - Scientific Analysis: The Holt-Winters model effectively captured periodic fluctuations in AOG incidents, demonstrating its ability to model seasonality. This makes it a robust tool for predicting cyclical patterns, critical for maintenance scheduling and resource planning.