## 5 Research Methodology and Data Analysis
### The Methodological Justification and Model Selection Criteria
The revised dataset was then evaluated against its original counterpart to assess performance improvements. Notably, the recall metric, which is essential for safety-critical applications like aviation maintenance forecasting, showed meaningful enhancement post-balancing. In parallel, we conducted an experimental application of random undersampling on the majority class to evaluate its comparative impact; however, the results indicated that SMOTE provided a more effective and stable uplift in minority class detection without significant information loss. These adjustments are reflected in the updated ROC-AUC scores that will be evaluated in the implication results and classification metrics discussed in the Logistic Regression Section of the manuscript. While SMOTE addressed immediate imbalance concerns, future investigations may incorporate ensemble-based resampling techniques such as SMOTEENN, which combines oversampling with cleaning methods, or integrate cost-sensitive learning approaches. To tackle these challenges, the airline should improve its predictive forecasting models, leveraging historical data to address AOG issues proactively. ARIMA and Holt-Winters: These models identify seasonal patterns and long-term trends in operational data, enabling SmartLynx to anticipate demand fluctuations and allocate resources more effectively. Logistic Regression: This model adds predictive capabilities by analyzing factors like part failure rates and maintenance history, precisely determining the likelihood of AOG events. Interpretation:  
The decrease in the meantime of delay hours per flight from 0.75 to 0.5 highlights better resolution efficiency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Mean (Hours)** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Min** & **Max** \\ \hline AOG Downtime & 3.5 & 0.6 & 2.9 & 4.3 \\ \hline Flight Delay per Incident & 0.75 & 0.2 & 0.5 & 1.1 \\ \hline Schedule Disruptions & 178 & 35.4 & 140 & 230 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Descriptive statistics of delays (2020–2024). The standard deviation of schedule disruptions (35.4) suggests variability based on seasonal operations, with Q2 and Q3 showing peaks in delays due to high ACMI demand. **Forecasting Algorithm for AOG Events and Delays**  
In Python, the following algorithms can be implemented to build a forecast model:  
ARIMA:  
* Suitable for time series forecasting with seasonality. * Algorithm: ARIMA models historical AOG data, identifying trends and seasonal components to predict future incidents. * AR (p): Number of lag observations. * I (d): Degree of differencing to achieve stationarity. * MA (q): Moving the average window. Exponential Smoothing (Holt-Winters Method):  
* Captures trends and seasonality in operational data (e.g., increased Q2/Q3 demand). * Algorithm: Holt-Winters models AOG incidents with trend and seasonality parameters. * Alpha (\(\alpha\)): Controls smoothing of level. * Beta (\(\beta\)): Controls smoothing of trend. * Gamma (\(\gamma\)): Controls seasonal adjustment. Logistic Regression (for binary AOG prediction):  
* Predicts the likelihood of AOG events occurring based on key factors (aircraft type, maintenance history, part failures). * Algorithm: Logistic Regression uses AOG incidents (Yes/No) as the dependent variable. **Python Workflow for Forecasting**  
Data Preparation:  
* Import reliability data into Python (AOG incidents, delays, downtime, flight disruptions). * Transform data to identify seasonal patterns (e.g., quarterly or annual peaks). Model Building:
* ARIMA Model: Set parameters p, d, q based on autocorrelation analysis. * Holt-Winters Model: Configure trend and seasonality parameters. * Logistic Regression: Train the model using aircraft type, part failure rates, and maintenance schedules as predictors. Model Evaluation: Use Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to assess model accuracy. Forecast Output:  
* Generate forecasts for future AOG incidents and delays.